Question: Deep multi-scale context analysis for improved salient object detection.

This research explores the influence of convolutional network depth on accuracy within the context of large-scale image recognition. Our key contribution lies in a comprehensive analysis of increasingly deep networks, demonstrating that substantial performance gains over state-of-the-art configurations can be achieved by extending the depth to 16-19 weight layers. This insight was pivotal to our ImageNet Challenge 2014 entry, where our team secured top positions in both the localisation and classification categories. Additionally, we showcase the generalizability of our deep representations to other datasets, achieving cutting-edge results. Notably, we have made our two highest-performing ConvNet models accessible to the public, enabling further investigation into the application of deep visual representations in the field of computer vision. This work highlights the significance of network depth in enhancing image recognition accuracy and the potential of deep learning techniques in tackling complex visual recognition tasks across various datasets.

Figure 1: In many applications, detection speed is as important as accuracy. (a) A standard pipeline for performing modern multiscale detection is to create a densely sampled image pyramid, compute features at each scale, and finally perform sliding window classification (with a fixed scale model). Although effective; the creation of the feature pyramid can dominate the cost of detection, leading to slow multiscale detection. (b) Viola and Jones [6] utilized shift and scale invariant features, allowing a trained detector to be placed at any location and scale without relying on an image pyramid. Constructing such a classifier pyramid results in fast multiscale detection; unfortunately, most features are not scale invariant, including gradient histograms, significantly limiting the generality of this scheme. (c) We propose a fast method for approximating features at multiple scales using a sparsely sampled image pyramid with a step size of an entire octave and within each octave we use a classifier pyramid. The proposed approach achieves nearly the same accuracy as using densely sampled image pyramids, with nearly the same speed as using a classifier pyramid applied to an image at a single scale.

The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris  (Mikolajczyk and  Schmid, 2002; Schaffalitzky and  Zisserman, 2002) and Hessian points  (Mikolajczyk and  Schmid, 2002), a detector of ‘maximally stable extremal regions', proposed by Matas et al. (2002); an edge-based region detector  (Tuytelaars and Van Gool, 1999) and a detector based on intensity extrema (Tuytelaars and Van Gool, 2000), and a detector of ‘salient regions', proposed by Kadir, Zisserman and Brady (2004). The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework.

In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.

In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model ``blends'' between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector on all classes, achieving 13% higher average AP. When employing the parts, we outperform the original DPM in $19$ out of $20$ classes, achieving an improvement of 8% AP. Furthermore, we outperform the previous state-of-the-art on VOC 2010 test by 4%.

Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://sstd.whuang.org/.

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation. Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8% in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-theart trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6% in median distance precision, while operating at real-time.

Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the “semantic gap” between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.

In this work we show that Deep Convolutional Neural Networks can outperform humans on the task of boundary detection, as measured on the standard Berkeley Segmentation Dataset. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art, from an optimal dataset scale F-measure of 0.780 to 0.808 while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. We also examine the potential of our boundary detector in conjunction with the higher level tasks of object proposal generation and semantic segmentation for both tasks our detector yields clear improvements over state-of-the-art systems.

In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.


Question: A multi-scale context integration method for deep reasoning in salient object detection.

This paper introduces an innovative framework for tackling the challenge of fine-grained visual categorization. The proposed method constructs a sophisticated model of the fundamental category that can be seamlessly adapted to images, yielding precise foreground segmentation and mid-level part localizations. This model is trained using the standard datasets for fine-grained categorization, which typically offer only a loose bounding box annotation around the object of interest (e.g., a bird) within each image. Both the segmentation and part localization outcomes are then utilized to encode the visual content of the image into a highly distinctive signature. The model's effectiveness is enhanced by its symbiotic nature: part localization is bolstered by segmentation, and simultaneously, segmentation benefits from part detection (such as part layout). Building upon the part-based object category detector developed by Felzenszwalb et al., and the robust Grab Cut segmentation algorithm by Rother et al., our framework introduces a straightforward spatial saliency coupling between the two components. Our evaluation demonstrates that this model outperforms current state-of-the-art methods in categorization accuracy. It also surpasses the performance of a comparable system that operates segmentation and part-localization processes independently.

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.

Understanding, reasoning, and manipulating semantic concepts of images have been a fundamental research problem for decades. Previous work mainly focused on direct manipulation on natural image manifold through color strokes, keypoints, textures, and holes-to-fill. In this work, we present a novel hierarchical framework for semantic image manipulation. Key to our hierarchical framework is that we employ structured semantic layout as our intermediate representation for manipulation. Initialized with coarse-level bounding boxes, our structure generator first creates pixel-wise semantic layout capturing the object shape, object-object interactions, and object-scene relations. Then our image generator fills in the pixel-level textures guided by the semantic layout. Such framework allows a user to manipulate images at object-level by adding, removing, and moving one bounding box at a time. Experimental evaluations demonstrate the advantages of the hierarchical manipulation framework over existing image generation and context hole-filing models, both qualitatively and quantitatively. Benefits of the hierarchical framework are further demonstrated in applications such as semantic object manipulation, interactive image editing, and data-driven image manipulation.

Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were initially developed for classifying images as a whole. While these networks exhibit outstanding recognition performance (i.e., what is visible?), they lack localization accuracy (i.e., where precisely is something located?). Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution. To alleviate this problem we propose a novel ResNet-like architecture that exhibits strong localization and recognition performance. We combine multi-scale context with pixel-level accuracy by using two processing streams within our network: One stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The other stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. Without additional processing steps and without pre-training, our approach achieves an intersection-over-union score of 71.8% on the Cityscapes dataset.

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called “part detector discovery” (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB2002011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_ discovery and https://github.com/cvjena/PartDetectorDisovery.

Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.

Learning powerful discriminative features for remote sensing image scene classification is a challenging computer vision problem. In the past, most classification approaches were based on handcrafted features. However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is only to use original RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show class activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using original RGB patches and attention map based class information provide complementary information to the standard RGB deep models. To the best of our knowledge, we are the first to investigate attention information encoded CNNs. Additionally, to enhance the discriminability, we further employ a recently developed object function called "center loss," which has proved to be very useful in face recognition. Finally, our framework provides attention guidance to the model in an end-to-end fashion. Extensive experiments on two benchmark datasets show that our approach matches or exceeds the performance of other methods.

We describe a two-stage method for detecting hands and their orientation in unconstrained images. The first stage uses three complementary detectors to propose hand bounding boxes. Each bounding box is then scored by the three detectors independently, and a second stage classifier learnt to compute a final confidence score for the proposals using these features. We make the following contributions: (i) we add context-based and skin-based proposals to a sliding window shape based detector to increase recall; (ii) we develop a new method of non-maximum suppression based on super-pixels; and (iii) we introduce a fully annotated hand dataset for training and testing. We show that the hand detector exceeds the state of the art on two public datasets, including the PASCAL VOC 2010 human layout challenge.

Capabilities of inference and prediction are the significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of models. Motivated by these observations, we propose a deep learning framework, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling. After that, a unified path-planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scenes and motion patterns, consequently improving the performance on visual path prediction task. In experiments, we extensively evaluate the model's performance by constructing two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the state-of-the-art approaches and owns a better generalization capability.

Image processing and pixel-wise dense prediction have been advanced by harnessing the capabilities of deep learning. One central issue of deep learning is the limited capacity to handle joint upsampling. We present a deep learning building block for joint upsampling, namely guided filtering layer. This layer aims at efficiently generating the high-resolution output given the corresponding low-resolution one and a high-resolution guidance map. The proposed layer is composed of a guided filter, which is reformulated as a fully differentiable block. To this end, we show that a guided filter can be expressed as a group of spatial varying linear transformation matrices. This layer could be integrated with the convolutional neural networks (CNNs) and jointly optimized through end-to-end training. To further take advantage of end-to-end training, we plug in a trainable transformation function that generates task-specific guidance maps. By integrating the CNNs and the proposed layer, we form deep guided filtering networks. The proposed networks are evaluated on five advanced image processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that the proposed approach runs 10-100Ã— faster and achieves the state-of-the-art performance. We also show that the proposed guided filtering layer helps to improve the performance of multiple pixel-wise dense prediction tasks. The code is available at https://github.com/wuhuikai/DeepGuidedFilter.

Faster RCNN is a region proposal based object detection approach. It integrates the region proposal stage and classification stage into a single pipeline, which has both rapid speed and high detection accuracy. However, when the model is applied to the target detection of remote sensing imagery, faced with multi-scale targets, its performance is degraded. We analyze the influences of pooling operation and target size on region proposal, then a modified solution for region proposal is introduced to improve recall rate of multi-scale targets. To speed up the convergence of the region proposal networks, an improved generation strategy of foreground samples is proposed, which could suppresses the generation of non-effective foreground samples. Extensive evaluations on the remote sensing image dataset show that the proposed model can obviously improve detection accuracy for multi-scale targets, moreover the training of the model is rapid and high-efficient.


Question: The influence of comments and recommendation systems on the purchasing behavior of online shoppers.

This paper explores the digital landscape's capacity to mirror and influence our social dynamics, specifically focusing on the Internet as a vast repository of personal information. From the content of individual web pages to the subscription lists of online forums, the digital footprint left by users often reflects their real-world social engagements. The study delves into the extraction and analysis of such data to forecast interpersonal relationships. It uncovers that certain types of information are more predictive of social ties than others, and that these predictive patterns can vary significantly across different user groups. This variability offers a unique window into the social fabric of various online communities. The methodologies developed here have significant practical implications, enabling the automatic detection of real-life connections and the identification, labeling, and profiling of virtual communities. These insights could revolutionize how we understand and engage with online social networks, potentially enhancing user experiences and facilitating the growth of meaningful digital communities.

**Question**: How does the implementation of a recommendation system affect consumer purchasing behavior in online retail environments?

**Documents**: The Role of Social Influence in Online Shopping
In this study, we examine the role of social influence on online shopping behavior. We use data from an online retailer that includes both purchase data and information about social relationships between customers. We find that social influence has a significant impact on online shopping behavior. Customers are more likely to purchase products that have been recommended by their social connections, and the effect is stronger for customers who have a high level of trust in their social connections. We also find that social influence has a stronger effect on the purchase of more expensive products. These results suggest that social influence is an important factor in online shopping behavior and that retailers should consider the role of social influence in their marketing strategies.

**Similar Documents**: This research investigates the pivotal role that social influence plays in shaping online shopping habits. By analyzing data from an e-commerce platform, which not only captures customer purchases but also details the social ties between buyers, the study reveals compelling insights. It demonstrates that the likelihood of a purchase increases when the item has been endorsed by one's social network, with this effect being particularly pronounced among individuals who place a high degree of trust in their social circles. Moreover, the study finds that social recommendations exert a greater influence on the acquisition of higher-priced items. These findings underscore the critical importance of social dynamics in online retail settings and suggest that businesses should integrate considerations of social influence into their strategic marketing approaches to effectively engage with consumers.

More and more E-commerce Websites provide products with different prices which made it hard for consumers to find the products and services they want. In order to overcome this data overload, personalized recommendation engines are used to suggest products and to provide consumers with relevant data to help them decide which products to purchase. Recommendation engines are highly computational and hence ideal for the Hadoop Platform. This system aims at building a book recommendation engine which uses item or user based recommendation from Mahout for recommending books. It will analyze the data and give suggestions based on what similar users did and on the past transaction history of the user.

Business analytics has evolved from being a novelty used by a select few, to an accepted facet of conducting business. Recommender systems form a cri tical component of the business analytics toolkit and, by enabling firms to effectively target custom ers with products and services, are helping alter t h ecommerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association ru le based methods being the most common. In this pap er, we propose a method to improve the quality of recom mendations made using association rules. This is accomplished by combining rules when possible, and stands apart from existing rule-combination methods in that it is strongly grounded in probabil ity theory. Combining rules requires the identifica t on of the best combination of rules from the many comb inations that might exist, and we use a maximumlikelihood framework to compare alternative combina tions. As it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context – the best solution corresponds to the set of rules that leads to the h ighest sum of mutual information associated with th e rules. Through a variety of experiments that evalua te the quality of recommendations made using the proposed approach, we show that (i) a greedy heuris tic u ed to solve the maximum likelihood estimation problem is very effective, providing results compar able to those from using the optimal set partitioni ng solution, (ii) the recommendations made by our appr oach are more accurate than those made by a variety of state-of-the-art benchmarks, including collabora tive filtering and matrix factorization, and (iii) the recommendations can be made in a fraction of a seco nd on a desktop computer, making it practical to us e in real-world applications.

The e-marketplace, or broker-managed online market, is one of today’s most profitable e-business models. In these marketplaces, buyers routinely engage with businesses and individual sellers with whom they have had little or no prior interaction, making trust one of the most important issues in the e-marketplaces. Therefore, a clear understanding of the trust building process in the e-marketplace is important for the success in the market. In this study, we analyze the process by which e-marketplace customers develop trust in both a market-maker and sellers. Additionally, this analysis provides a theoretical framework to identify the antecedents of trust. In the framework, we suggest that the impact of trust on transaction intention is moderated by the customer’s previous experiences in the e-marketplace. The theoretical model presented here was tested on survey and transaction data collected from 692 respondents. The results indicate that customers value the importance of trust in the market-maker and sellers differentially by the level of transaction experience. It is also shown that market-maker’s characteristics (reputation, website’s usability and security) and seller’s characteristics (expertise) play an important role in the formation and development of trust toward a market-maker and sellers, respectively. Journal of Information Technology (2007) 22, 119–132. doi:10.1057/palgrave.jit.2000095 Published online 23 January 2007

Using the means-end framework as a theoretical foundation, this article conceptualizes, constructs, refines, and tests a multiple-item scale (E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online. Two stages of empirical data collection revealed that two different scales were necessary for capturing electronic service quality. The basic E-S-QUAL scale developed in the research is a 22-item scale of four dimensions: efficiency, fulfillment, system availability, and privacy. The second scale, E-RecS-QUAL, is salient only to customers who had nonroutine encounters with the sites and contains 11 items in three dimensions: responsiveness, compensation, and contact. Both scales demonstrate good psychometric properties based on findings from a variety of reliability and validity tests and build on the research already conducted on the topic. Directions for further research on electronic service quality are offered. Managerial implications stemming from the empirical findings about E-S-QUAL are also discussed.

This paper explores the impact of the quality of a web site on the cognitive process leading to consumers’ emotions—considered as direct antecedents to shopping behaviors and operationalized as mental states of readiness arising from the appraisal of events. A parsimonious theoretical model was defined and tested with data collected from 215 web-shopping episodes during which consumers were shopping for low-touch products. Analysis of the results showed that web site quality had a positive impact on the cognitive appraisal of situational state, which in turn influenced five of the six emotions of the proposed model: liking, joy, pride, dislike, and frustration. Results also showed that a substantial number of shoppers experienced intensively the emotions of liking and joy. Moreover, this paper highlights several implications that could help managers and webmasters improve the quality of their web sites. # 2006 Elsevier B.V. All rights reserved.

Based on the Technology Acceptance Model and related literatures, this paper proposes an integrated conceptual model of consumers' initial trust in B2C e-business. Empirical research results show that, perceived usefulness, perceived safety, perceived website reputation and propensity to trust significantly positively affect consumers' initial trust in e-vendor, which further positively influence consumers' intention to purchase online, by the complete mediation of attitude; however, the effect of perceived use of use on initial trust doesn't show the statistic significance. Furthermore, there are significant gender differences in the effects of perceived ease of use, perceived safety, perceived website reputation on consumers' initial trust.

This paper explores search behavior of online shoppers. Information economics literature suggests that search cost in electronic markets has essentially been reduced to zero as consumers are able to use powerful search tools free of charge to easily find and compare product and shopping information on the Internet. In the present research, however, we present a research model proposing that users need to spend time and effort when completing search tasks resulting in significant search cost and a trade-off between search cost and search performance. Preliminary findings from an Internet experiment indicate that search task complexity, search engine capability, search strategy and user experience are important factors determining search cost and performance.

Acknowledgement I am greatly indebted to Omar El Sawy and Ann Majchrzak for their guidance and suggestion, and I would like to thank Ricky Lim and Raymond for their help with the data analysis. Abstract Consumers' trust in their online transactions is vital for the sustained progress and development of electronic commerce. Our paper proposes that in addition to known factors of trust such a vendor's reputation, consumers' perception of privacy and security influence their trust in online transactions. Our research shows that consumers exhibit variability in their perceptions of privacy, security and trust between online and offline transactions even if it is conducted with the same store. We build upon this finding to develop and validate measures of consumers' perceived privacy and perceived security of their online transactions which are then theorized to influence their trust in EC transactions. We propose that the perceptions of privacy and security are factors that affect the consumers' trust in the institutional governance mechanisms underlying the Internet. We conduct two distinct empirical studies and through successive refinement and analysis using the Partial Least Squares technique, we test our hypothesized relationships while verifying the excellent measurement properties associated with our instrument. Our study finds that the consumers' perceived privacy and perceived security are indeed distinct constructs but the effect of perceived privacy on trust in EC transactions is strongly mediated by perceived security. A major implication of our findings is that while the much studied determinants of trust such as reputation of the transacting firm should not be neglected, vendors should also engage in efforts to positively influence consumer perceptions of privacy and security. We discuss the significance of this observation in the context of increasing importance in acquiring customer information for personalization and other online strategies.

Internet-based group-buying has become a new growth point for China's e-commerce. By studying B2C group-buying model, this paper has collected data of 20 major Chinese group-buying websites, studied factors that might influence Internet users' group-buying behavior, proposed a new model for evaluating the value of Internet enterprises, and provided a feasible reference model for the evaluation and performance of Internet enterprises.

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation allows to increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improuvements are achieved for new users, who provided few ratings.


Question: The influence of comments and recommendation systems on the purchasing behavior of online shoppers.

In the context of electronic commerce, the advent of interactive media, like the World Wide Web, has revolutionized the way consumers search for information and shop online. However, understanding the nuances of consumer decision-making in these environments remains limited. One distinguishing feature of online shopping is the ability for retailers to create highly interactive interfaces, offering tools that cater to individual consumer preferences, potentially transforming how shoppers seek product information and make choices. This paper explores the impact of interactive decision aids on consumer decision-making in online shopping contexts.

Interactive decision aids, designed to assist shoppers by customizing the shopping experience, can significantly influence the decision-making process. Typically, consumers engage in a two-stage process: first, screening a broad range of products to shortlist potential options, and second, conducting a detailed comparison of these options before making a purchase. Interactive tools that support these stages are invaluable. For instance, a recommendation system can efficiently filter through a vast array of alternatives based on consumer preferences, generating a personalized list of recommended products. A comparison matrix, on the other hand, aids in detailed comparisons among selected products by organizing attribute information and enabling sorting by various criteria.

Theoretical and empirical research from marketing, psychology, decision-making, and decision support systems underpin the hypotheses developed in this paper. These hypotheses explore how the use of recommendation systems and comparison matrices affects consumer information search, the size and quality of consideration sets, and decision quality in online shopping environments. An experimental study using a simulated online store was conducted to test these hypotheses.

The findings reveal that both interactive tools significantly influence consumer decision-making. Recommendation systems reduce the effort required for product information search, streamline consideration sets, and enhance decision quality. Similarly, comparison matrices decrease the size of consideration sets while increasing their quality, and positively impact decision quality indicators. These results suggest that interactive tools designed to assist in the initial product screening and facilitate detailed comparisons can markedly improve the quality and efficiency of purchase decisions, allowing shoppers to make better choices with less effort.

In conclusion, interactive decision aids have the potential to fundamentally alter consumer behavior in online shopping environments, enhancing both the decision-making process and the overall shopping experience.

M oving some Web consumers along to the purchase click is proving to be difficult, despite the impressive recent growth in online shopping. Consumer online shopping revenues and related corporate profits are still meager, though the industry is optimistic, thanks to bullish forecasts of cyberconsumer activity for the new millennium. In 1996, Internet shopping revenues for U.S. users, excluding cars and real estate, were estimated by Jupiter Communications , an e-commerce consulting firm in New York, at approximately $707 million but are expected to How merchants can win back lost consumer trust in the interests of e-commerce sales. reach nearly $37.5 billion by 2002 [1]. Meanwhile, the business-to-business side is taking off with more than $8 billion in revenues for 1997 and $327 billion predicted by 2002 just in the U.S., according to For-rester Research, an information consulting firm in Cambridge, Mass. [4]. On the consumer side, a variety of barriers are invoked to explain the continuing difficulties. There are, to be sure, numerous barriers. Such factors as the lack of standard technologies for secure payment, and the lack of profitable business models play important roles in the relative dearth of commercial activity by businesses and consumers on the Internet compared to what analysts expect in the near future. Granted, the commercial development of the Web is still in its infancy, so few expect these barriers to commercial development to persist. Still, commercial development of the Web faces a far more formidable barrier—consumers' fear of divulging their personal data—to its ultimate com-mercialization. The reason more people have yet to shop online or even provide information to Web providers in exchange for access to information, is the fundamental lack of faith between most businesses and consumers on the Web today. In essence, consumers simply do not trust most Web providers enough to engage in " relationship exchanges " involving money and personal information with them. Our research reveals that this lack of trust arises from the fact that cyberconsumers feel they lack control over the access that Web merchants have to their

Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

There has been a great increase in interests towards online shopping. In case of purchase of products like apparels which always require a sense of knowledge on how cloths would fit upon a person. This is the major reason why less number of apparels are being shopped online. Hence, a virtual dressing room which would make people know how cloths personally fits in would be a great luxury for the online sellers which could give a wide choice for customers. For online marketers, this would be a great tool for enhancing its market.

When it comes to buying expensive goods people expect to be skillfully steered through the options by well-informed sales assistants that are capable of balancing the user’s many and varied requirements. In addition users often need to be educated about the product-space, especially if they are to come to understand what is available and why certain options are being recommended by the sales-assistant. The same issues arise in interactive recommender systems, our online equivalent of a sales assistant and explanation in recommender systems, as a means to educate users and justify recommendations, is now well accepted. In this paper we focus on a novel approach to explanation. Instead of attempting to justify a particular recommendation we focus on how explanations can help users to understand the recommendation opportunities that remain if the current recommendation should not meet their requirements. We describe how this approach to explanation is tightly coupled with the generation of compound critiques, which act as a form of feedback for the users. And we argue that these explanation-rich critiques have the potential to dramatically improve recommender performance and usability.

Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.

Amazon sales-rank gives a relative estimate of a product item’s popularity among other items in the same category. An early prediction of the Amazon sales-rank of a product would imply an early guess of its sales-popularity relative to the other products on Amazon, which is one of the largest e-commerce hub across the globe. Traditional methods suggest use of product review related features, e.g., volume of reviews, text content of the reviews etc. for the purpose of prediction. In contrast, we propose in this paper for the first time a network-assisted approach to construct suitable features for prediction. In particular, we build a co-purchase network treating the individual products as nodes, with edges in between if two products are bought with one another. The way a product is positioned in this network (e.g., its centrality, clustering coefficient etc.) turns out to be a strong indicator of its sales-rank. This network-assisted approach has two distinct advantages over the traditional baseline method based on review analysis – (i) it works even if the product has no reviews (relevant especially in the early stages of the product launch) and (ii) it is notably more discriminative in classifying a popular (i.e., low sales-rank) product from an unpopular (i.e., high sales-rank) one. Based on this observation, we build a supervised model to early classify a popular product from an unpopular one. We report our results on two different product categories (CDs and cell phones) and obtain remarkably better classification accuracy compared to the baseline scheme. When the top 100 (700) products based on sales-rank are labelled as popular and the bottom 100 (700) are labelled as unpopular, the classification accuracy of our method is 89.85% (82.1%) for CDs and 84.11% (84.8%) for cell phones compared to 46.37% (68.75%) and 83.17% (71.95%) respectively from the baseline method.

Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.

Before reaping transaction cost savings from the Internet, online sellers must credibly communicate the quality of their goods or their reliability of delivery. We use baseball cards as an example to address (1) whether consumers fully understand the risk of online trading, (2) the extent to which consumers employ seller reputation and professional grading to alleviate the risk they are exposed to, and (3) the impact of online trading on the retail market and supporting industries. An experiment is carried out to obtain actual baseball cards from both online and retail markets whose quality is then professionally graded and compared to the prices paid by online buyers for goods with similar claims. Our findings indicate that some uninformed online buyers are misled by non-credible claims of quality; they pay higher prices but do not receive better quality and in fact are defrauded more often. Although a combination of professional grading and reputation mechanisms appear to have the potential to solve the problem, they have only had limited success so far. Data on structural changes in the baseball card industry suggest the problems stemming from buyer misconception may have far-reaching consequences, extending beyond the immediate losses of defrauded consumers to influencing producer and supply decisions. Our findings also suggest that empirical studies using online price data should be careful of what assumptions they are making of online users and what behaviors of buyers and sellers are likely to drive the price data. Department of Economics, University of Maryland, 3115 G Tydings, College Park, MD 20742. Contact Email: jin@econ.umd.edu. We thank Seth Sanders, John List, John Shea, Dan Vincent, David Lucking-Reiley, Larry Ausubel, Peter Cramton, V. Joseph Hotz, Jimmy Chan, Vincent Crawford, attendants in Johns Hopkins UMD conference and participants of UCLA,UCSD and FTC seminars for constructive advice and suggestions. We are grateful to 8 friends who acted as our agents in purchasing baseball cards in retail markets, and numerous sports card store owners for their time and insights regarding the sports card industry. Excellent research assistance from Randy Alexander Moore and Krzysztof Fizyta is gratefully acknowledged. Any remaining errors are ours.

The global nature of communication and shopping has as well redefined, seeing that it is the perfect vehicle for online shopping stores. Online convenient shop is mostly reflected in shorter time and less energy spent, including shipping cost reduction, less crowd and queues than real markets, unlimited time and space, which all increase convenience of shopping. Internet shopping for businesses and consumers are being accepted as an alternative shop mode rather than visiting the stores. However, convincing the consumers to shop online is still a challenging task for web retailers in Malaysia. The growth of Internet technology in Malaysia has enormous potential as it reduces the costs of product and service delivery and extends geographical boundaries in bringing buyers and sellers together. This study is conducted to identify factors influencing consumers towards online shopping in Malaysia. The study focused on nine independent variables namely appearance, quick loading, security, sitemap, validity, promotion, attractiveness, believability, and originality. We applied Five-point Likert Scale to measure the influential factors on intention for online shopping.

Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer's interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.


Question: The influence of comments and recommendation systems on the purchasing behavior of online shoppers.

This paper presents an innovative methodology for the efficient identification of web communities, which is crucial for understanding online shopper behavior. The approach focuses on analyzing the structure and dynamics of online platforms to pinpoint communities that form around shared interests or activities. By identifying these communities, we can better comprehend the impact of comments and recommendation systems on online shopper buying behavior. The methodology employs advanced algorithms to detect patterns and connections within vast datasets, revealing the underlying networks of interactions. These networks are then analyzed to uncover the communities where users engage in discussions, share recommendations, and influence each other's purchasing decisions. The findings from this research can be applied to enhance the design of recommendation systems, making them more effective in guiding users towards products that align with their preferences and needs. Moreover, understanding the role of comments within these communities can help in developing strategies to foster positive user experiences and improve overall customer satisfaction. The study's results also shed light on the evolving nature of online communities and the factors that contribute to their formation and sustainability. This knowledge is invaluable for businesses aiming to optimize their online presence and tap into the potential of community-driven marketing.

The paper's approach to identifying web communities is grounded in the analysis of complex network structures, utilizing algorithms to detect patterns of interaction and connectivity. By focusing on online platforms where users engage in discussions, share recommendations, and influence each other's buying decisions, the methodology offers insights into the dynamics of online shopper behavior. The efficient identification of these communities is achieved through the application of sophisticated data analysis techniques, which enable the detection of subgroups within larger networks. This approach not only enhances our understanding of how online communities form but also informs the development of recommendation systems and strategies to improve user engagement. The research findings contribute to the broader field of online marketing by providing a deeper understanding of the role of comments and recommendations in shaping consumer behavior. Moreover, the methodology's ability to uncover the hidden structures within web communities offers a powerful tool for businesses seeking to optimize their online strategies and foster positive user experiences. By leveraging the insights gained from this study, companies can better engage with their target audiences, enhance customer satisfaction, and ultimately drive sales through community-driven marketing efforts.

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.
 In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.

Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: wendy@marketing.wharton.upenn.edu

Web-based Group-Buying mechanisms, a refinement of quantity discounting, are being used for both Business-to-Business (B2B) and Business-toConsumer (B2C) transactions. In this paper, we survey currently operational online Group-Buying markets, and then study this phenomenon using analytical models. We surveyed over fifty active Group-Buying sites, and provide a comprehensive review of Group-Buying practices in the B2B, B2C and non-profit sectors, across three continents. On the modeling side, we build on the coordination literature in Information Economics and the quantity-discounts literature in Operations to develop an analytical model of a monopolist who uses web-based Group-Buying mechanisms under different kinds of demand uncertainty. We derive the monopolist's optimal Group-Buying schedule, and compare his profits with those that obtain under the more conventional posted-price mechanism. We also study the effect of heterogeneity in the demand regimes, in combination with uncertainty, on the relative performance of the two mechanisms. We further study the impact of the timing of the pricing decision (vis-à-vis the production decision) by modeling it as a two-stage game between the monopolist and buyers. Finally, we investigate how Group-Buying schemes compare with posted price markets when buyers can revise their prior valuation of products based on information received from third parties (infomediaries). In all cases, we characterize the conditions under which one mechanism outperforms the other, and those under which the posted price and Group-Buy mechanisms lead to identical seller revenues. Our results have implications for firms' choice of price discovery mechanisms in electronic markets and scheduling of production and pricing decisions in the presence (and absence) of scale economies of production. 1 Email: anandk@wharton.upenn.edu; Tel: (215) 898 1175. 2 Email: raviaron@wharton.upenn.edu; Tel:(215) 573 5677.

Critiquing-based recommender systems elicit users’ feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users’ profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.

Different from traditional firms, online retailers do not often produce common goods on their own. On the contrary, they sell merchandises that traditional firms produce. Furthermore, online retailers are also different from traditional retailers because the former do not often have frontline employees, physical outlets, or displaying layouts. Therefore, when building a strong online retailer brand, it is not enough to only consider the traditional dimensions of customer-based brand equity, namely brand awareness, perceived quality, brand associations, and brand loyalty. Some dimensions, i.e. trust associations and emotional connection need to be distinguished or included in the context of online business. Besides, the willingness to pay a price premium is always being viewed as a good indicator to measure brand equity, but for online retailers, it is conditional on some premises. By structural equation modeling, it was verified that for online retailers, the importance of brand awareness and trust associations decreased but perceived quality remained its strong influence. Brand loyalty was the better indicator for measuring brand equity, and emotional connection became so critical that if without it, customers would lack willingness to pay a price premium.

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation allows to increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improuvements are achieved for new users, who provided few ratings.

The rapid development of technology today makes Internet users continues to increase. This is supported by the ease of internet users access the internet either through a PC, laptop, mobile phones, tablets and other media. The increase in Internet users this makes the internet into a proper promotion using online advertising. With a wide reach and touch the various layers of the Internet media communities may be appropriate advice for company promotion. However, an increasing number of Internet users, especially in the city of Makassar is not accompanied by an increase in the number of online purchases. Based on that it is necessary to examine how the effect of online advertising on consumer buying behavior and online, as well as how to control the behavior and subjective norms influence the attitudes and interests of consumers buy online. This study aims to analyze and test the effect of online advertising on consumer attitudes and purchase interest online, a survey conducted on students of Internet users in the city of Makassar. The study was conducted on students of public and private universities in the city of Makassar. The method used was a quantitative analysis using the technique of purposive sampling method with a sample of 340 people. Testing this hypothesis using structural equation modeling (SEM). The results showed that online advertising has an influence on consumer buying behavior and online. Dimensions interactivity of online advertising provides the highest influence on the attitudes and interests of consumers purchasing online.

This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their impact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a subset of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer evaluation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are effective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.

This paper explores search behavior of online shoppers. Information economics literature suggests that search cost in electronic markets has essentially been reduced to zero as consumers are able to use powerful search tools free of charge to easily find and compare product and shopping information on the Internet. In the present research, however, we present a research model proposing that users need to spend time and effort when completing search tasks resulting in significant search cost and a trade-off between search cost and search performance. Preliminary findings from an Internet experiment indicate that search task complexity, search engine capability, search strategy and user experience are important factors determining search cost and performance.


Question: The influence of comments and recommendation systems on the purchasing behavior of online shoppers.

This paper explores the role of information systems in mitigating buyer search costs within electronic marketplaces, thereby influencing the dynamics of differentiated product markets. Electronic marketplaces, acting as intermediaries between buyers and sellers, significantly lower the barriers for buyers to gather information about seller prices and product details. This reduction in search costs leads to a decrease in seller's ability to charge monopolistic prices and enhances market efficiency by optimizing resource allocation. The study models the effect of diminished search costs in a differentiated product market, formalizing the allocative efficiencies that arise from such reductions in an electronic marketplace context. It further investigates the implications for investment incentives among buyers, sellers, and independent intermediaries in electronic marketplaces. The paper also introduces the concept of separating price information from product attribute information, discussing the design of markets that foster competition on both fronts, thereby enriching the marketplace ecosystem. Copyright © 1997 by The Institute of Management Sciences

**Question**: The Role of Blockchain Technology in Enhancing Supply Chain Transparency

**Documents**: Blockchain Technology for Improved Supply Chain Finance Processes
Supply chain finance (SCF) is a set of techniques that improves the efficiency of the working capital and cash flow of companies, by optimally managing the receivables, payables and inventory of companies. SCF is an important concept for financial institutions, as it enables them to extend their services to smaller companies and to offer innovative financial products. However, SCF is also a complex process that involves multiple parties and requires a high level of trust and transparency. The current SCF process is based on paper-based documentation and manual reconciliation, which leads to inefficiencies and errors. In this paper, we propose the use of blockchain technology to improve the SCF process. Blockchain technology is a distributed ledger technology that enables secure, transparent and immutable transactions. We present a blockchain-based SCF process that addresses the challenges of the current SCF process. Our proposed process uses smart contracts to automate the SCF process and to ensure that all parties involved in the transaction are in agreement. We also present a proof-of-concept implementation of our proposed process using the Ethereum blockchain platform. The results of our implementation show that blockchain technology can significantly improve the efficiency and transparency of the SCF process.

**Similar Documents**: This paper advocates the integration of blockchain technology to enhance the efficiency and transparency of supply chain finance (SCF) processes. SCF, a critical mechanism for improving working capital and cash flow management, often faces challenges due to its complexity and the need for trust and transparency among multiple stakeholders. The traditional

The rapid development of technology today makes Internet users continues to increase. This is supported by the ease of internet users access the internet either through a PC, laptop, mobile phones, tablets and other media. The increase in Internet users this makes the internet into a proper promotion using online advertising. With a wide reach and touch the various layers of the Internet media communities may be appropriate advice for company promotion. However, an increasing number of Internet users, especially in the city of Makassar is not accompanied by an increase in the number of online purchases. Based on that it is necessary to examine how the effect of online advertising on consumer buying behavior and online, as well as how to control the behavior and subjective norms influence the attitudes and interests of consumers buy online. This study aims to analyze and test the effect of online advertising on consumer attitudes and purchase interest online, a survey conducted on students of Internet users in the city of Makassar. The study was conducted on students of public and private universities in the city of Makassar. The method used was a quantitative analysis using the technique of purposive sampling method with a sample of 340 people. Testing this hypothesis using structural equation modeling (SEM). The results showed that online advertising has an influence on consumer buying behavior and online. Dimensions interactivity of online advertising provides the highest influence on the attitudes and interests of consumers purchasing online.

The Internet and World Wide Web have brought us into a world of endless possibiliti es: interactive Web sites to experience, music to li sten to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.

Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer's interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: wendy@marketing.wharton.upenn.edu

Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique, to realize product item recommendation. Overall, the present CF recommendation can perform very well, if the target user owns similar friends (user-based CF), or the product items purchased and preferred by target user own one or more similar product items (item-based CF). While due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be both absent from the user-product purchase network, which lead to a big challenge to recommend appropriate product items to the target user. Considering the challenge, we put forward a Structural Balance Theory-based Recommendation (i.e., SBT-Rec) approach. In the concrete, (I) user-based recommendation: we look for target user's “enemy” (i.e., the users having opposite preference with target user); afterwards, we determine target user's “possible friends”, according to “enemy's enemy is a friend” rule of Structural Balance Theory, and recommend the product items preferred by “possible friends” of target user to the target user. (II) likewise, for the product items purchased and preferred by target user, we determine their “possibly similar product items” based on Structural Balance Theory and recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on MovieLens-1M dataset.

Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.

A core value in recommender systems is personalization, the idea that the recommendations produced are those that match the user’s preferences. However, in many real-world recommendation contexts, the concerns of additional stakeholders may come into play, such as the producers of items or those of the system owner. Some researchers have examined special cases of such concerns, for example, in reciprocal recommendation. However, there has not been a comprehensive treatment of the integration of multiple stakeholders into recommendation calculations. The paper suggests a utility-based framework for representing stakeholder values in recommendation actions and calculating a multidimensional utility. We demonstrate how a standard algorithm performs in a simulation of a multi-stakeholder recommendation task requiring on-line optimization, and show that a simple greedy approach can lead to enhanced overall utility with minimal loss of accuracy for users.

The proliferation of E-commerce sites has made web an excellent source of gathering customer reviews about products; as there is no quality control anyone one can write anything which leads to review spam. This paper previews and reviews the substantial research on Review Spam detection technique. Further it provides state of art depicting some previous attempt to study review spam detection. KeywordsNatural language processing, reviews centric spam detection, reviewer centric spam detection

Identifying factors that influence customers’ e-loyalty is paramount for practitioners and academics to develop successful marketing strategies and behavioral models. Online bookselling is a rapid growing industry in the UK, where e-loyalty models have yet to reach a conclusive argument. This paper aims to explore factors influencing customers’ e-loyalty to five online bookselling websites in the UK by testing a theoretical model, based on expectation-confirmation theory. A quantitative approach was employed using questionnaires; the sample consisted of 290 respondents (50% males, age range: 18 to over 54). The questionnaire was pretested and confirmatory factor analysis was performed to assess the measurement model. Structural equation modeling and MANOVA were employed to examine the association between latent constructs. Eleven hypotheses were formulated examining different independent variables in the theoretical model; results showed a significant direct and positive association between satisfaction and e-loyalty. Web design affected e-loyalty significantly on all bookselling websites, perceived value was a significant predictor of satisfaction while price notably influenced e-trust development. Etrust was not associated with e-loyalty. This study introduces new variables that affect e-loyalty as well as illuminates new associations between existing factors – perceived value, price, and trust are new aspects which practitioners and academics should take into account for marketing strategies and behavioral models, respectively. Hence, managers will likely increase customer satisfaction and loyalty by improving web design and balancing quality with price, predisposing positively customer’s attitudes towards the website.

Acknowledgement I am greatly indebted to Omar El Sawy and Ann Majchrzak for their guidance and suggestion, and I would like to thank Ricky Lim and Raymond for their help with the data analysis. Abstract Consumers' trust in their online transactions is vital for the sustained progress and development of electronic commerce. Our paper proposes that in addition to known factors of trust such a vendor's reputation, consumers' perception of privacy and security influence their trust in online transactions. Our research shows that consumers exhibit variability in their perceptions of privacy, security and trust between online and offline transactions even if it is conducted with the same store. We build upon this finding to develop and validate measures of consumers' perceived privacy and perceived security of their online transactions which are then theorized to influence their trust in EC transactions. We propose that the perceptions of privacy and security are factors that affect the consumers' trust in the institutional governance mechanisms underlying the Internet. We conduct two distinct empirical studies and through successive refinement and analysis using the Partial Least Squares technique, we test our hypothesized relationships while verifying the excellent measurement properties associated with our instrument. Our study finds that the consumers' perceived privacy and perceived security are indeed distinct constructs but the effect of perceived privacy on trust in EC transactions is strongly mediated by perceived security. A major implication of our findings is that while the much studied determinants of trust such as reputation of the transacting firm should not be neglected, vendors should also engage in efforts to positively influence consumer perceptions of privacy and security. We discuss the significance of this observation in the context of increasing importance in acquiring customer information for personalization and other online strategies.