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The papers in this special section focus on innovation in design processes. Design is the human process of applying knowledge to create novel and useful artefacts, such as new products, services, and systems with economic and societal values. In practice and literature, the relevant terms and definitions are often blurred. The design process is often referred to as the "new product development" process, "innovation process", "design innovation" process, etc. Not all design processes lead to inventions or innovations. The design processes that yield innovation are often coupled with the scientific research process and the entrepreneurship process. Herein our focus is not on the innovation arising from the design process, but on the innovations in and of a design process itself that can increase innovation success from the design process. [ABSTRACT FROM AUTHOR]

The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world’s largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals. [ABSTRACT FROM AUTHOR]

Decision support is a vital function in electronic commerce (e-commerce). The purpose of this paper is to construct a review-based decision support model for items comparison in e-commerce. The proposed model uses probability multivalued neutrosophic linguistic numbers (PMVNLNs) to characterize online reviews. It overcomes the limitation of existing models by considering neutral information and hesitancy in text reviews. The fuzzy characterization of reviews (i.e., PMVNLN) can reflect similarities and differences in positive (negative) information. In addition, the model considers consumers’ bounded rational behaviors by combining the regret theory with an outranking method. We empirically compare the proposed model with models in http://PConline.com and four existing models with data from http://PConline.com. The performance of these models in terms of accuracy is measured by the total relative difference metric. Results indicate the good performance of the proposed model. Our model is a promising option for e-commerce to provide consumers with good decision support service. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers. [ABSTRACT FROM PUBLISHER]

The success of online shopping accelerates the development of express delivery business with economic and efficient service. Current express delivery systems usually deliver the packages in noncooperation mode and cannot jointly optimize the express fee and moving cost of users. This paper proposes the cooperative package assignment system by lumping packages at the same express station to share the express fee, and proposes a novel pricing structure to stimulate the express stations to join the system without revenue loss by introducing cooperation cost. We formulate the cooperative package assignment (CPA) problem with heterogeneous express stations for joint optimization of users’ express fee and moving cost. Then, an approximate algorithm, CPAA, is proposed for the CPA problem based on the greedy approach using submodular function minimization. We show that the designed algorithm achieves computational efficiency and guaranteed approximation. Furthermore, we model the large-scale CPA problem as CPA-game and present a game theoretic algorithm, CPAGA. We show that CPA-game has at least one Nash Equilibrium, and CPAGA finally converges to a pure Nash Equilibrium. Through extensive simulations, we demonstrate that CPAA and CPAGA show great advantages in terms of comprehensive cost, which is 28.1% and 19.9% lower than that in noncooperation mode on average, respectively. Moreover, CPAGA shows great scalability and is more suitable for large-scale cooperative package assignment systems. [ABSTRACT FROM AUTHOR]

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, logistics industry becomes a crucial component in the e-commercial ecological chain. Impelled by both economical and environmental benefit, logistics companies demand automated tools more urgently than ever. In this paper, a dynamic logistic dispatching system is proposed. The underlying model of the dispatching system is the dynamic vehicle routing problem which allows new orders being received as the working day progress. With this feature, the system becomes more practical than the systems with traditional static vehicle routing models, but is also more challenging as the vehicles must be scheduled in a dynamic way. The core of the system is a specially designed set-based particle swarm optimization algorithm. According to the characteristic of the problem, a new encoding scheme is defined by set and possibility, and a local refinement method is designed to accelerate the convergence speed of the algorithm. In addition, two more techniques: 1) region partition and 2) archive strategy are incorporated in the dispatching system to reduce the complexity of the problem and to facilitate the optimization process, helping the dispatcher control the vehicles in real time. The proposed system is tested on various benchmarks with different scales. Experimental results show that the proposed dispatching system is effective. [ABSTRACT FROM AUTHOR]

City-wide package delivery has become popular due to the dramatic rise of online shopping. It places a tremendous burden on the traditional logistics industry, which relies on dedicated couriers and is labor-intensive. Leveraging the ridesharing systems is a promising alternative, yet existing solutions are limited to one-hop ridesharing or need consignment warehouses as relays. In this paper, we propose a new package delivery scheme which takes advantage of multi-hop ridesharing and is entirely consignment free. Specifically, a package is assigned to a taxi which is guided to deliver the package all along to its destination while transporting successive passengers. We tackle it with a two-phase solution, named PPtaxi. In the first phase, we use the Multivariate Gaussian distribution and Bayesian inference to predict the passenger orders. In the second phase, both the computation efficiency and solution effectiveness are considered to plan package delivery routes. We evaluate PPtaxi with a real-world dataset from an online taxi-taking platform and compare it with multiple benchmarks. The results show that the successful delivery rate of packages with our solution can reach 95 percent on average during the daytime, and is at most 46.9 percent higher than those of the benchmarks. [ABSTRACT FROM AUTHOR]

In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RaPare ) to learn the latent profiles of cold-start users/items. The centerpiece of our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RaPare strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario. [ABSTRACT FROM PUBLISHER]

Big data analytics workloads are very significant ones in modern data centers, and it is more and more important to characterize their representative workloads and understand their behaviors so as to improve the performance of data center computer systems. In this paper, we embark on a comprehensive study to understand the impacts and performance implications of the big data analytics workloads on the systems equipped with modern superscalar out-of-order processors. After investigating three most important application domains in Internet services in terms of page views and daily visitors, we choose 11 representative data analytics workloads and characterize their micro-architectural behaviors by using hardware performance counters. Our study reveals that the big data analytics workloads share many inherent characteristics, which place them in a different class from the traditional workloads and the scale-out services. To further understand the characteristics of big data analytics workloads, we perform correlation analysis to identify the most key factors that affect cycles per instruction (CPI). Also, we reveal that the increasing complexity of the big data software stacks will put higher pressures on the modern processor pipelines. [ABSTRACT FROM AUTHOR]

The twelve papers in this special issue presents relevant research contributions from the disciplines of finance, mathematics, data science and engineering to facilitate scientific cross-fertilization. It will also serve the signal processing community to be exposed to the state of the art in mathematical finance, financial engineering, financial signal processing an electronic trading, and to foster future research in this emerging area. The main themes of this special issue include using tools from machine learning and signal processing that help to address some of the main problems arising in quantitative finance: modeling risk and correlations of financial instruments and their baskets, returns and liquidity, and problems involving risk-aware resource allocation -namely portfolio optimization. These problems involve tools from convex and discrete optimization, non-parametric statistics, time-series modeling,graph theory and high-dimensional covariance estimation. [ABSTRACT FROM PUBLISHER]

This paper introduces how to infer trust relationships from billion-scale networked data to benefit Alibaba E-Commerce business. To effectively leverage the network correlations between labeled and unlabeled relationships to predict trust relationships, we formalize trust into multiple types and propose a graphical model to incorporate type-based dyadic and triadic correlations, namely eTrust. We also present a fast learning algorithm in order to handle billion-scale networks. Systematically, we evaluate the proposed methods on four different genres of datasets with labeled trust relationships: Alibaba, Epinions, Ciao, and Advogato. Experimental results show that the proposed methods achieve significantly better performance than several comparison methods (+1.7-32.3% by accuracy; $p<<0.01$ p < < 0. 01 , with $t$ t -test). Most importantly, when handling the real large networked data with over 1,200,000,000 edges (Ali-large), our method achieves 2,000× speedup to infer trust relationships, comparing with the traditional graph learning algorithms. Finally, we have applied the inferred trust relationships to Alibaba E-commerce platform: Taobao, and achieved 2.75 percent improvement on gross merchandise volume (GMV). [ABSTRACT FROM AUTHOR]

Online reviews and comments after product sales have become very important for making buying and selling decisions. Fake reviews will affect such decisions due to deceptive information, leading to financial losses for the consumers. Identification of fake reviews has thus received a great deal of attention in recent years. However, most websites have only focused on dealing with problematic reviews and comments. Amazon and Yelp would only remove possible fake reviews without questioning the sellers who could continue posting deceptive reviews for business purposes. In this paper, we propose a method for the detection of fake reviews based on review records associated with products. We first analyze the characteristics of review data using a crawled Amazon China dataset, which shows that the patterns of review records for products are similar in normal situations. In the proposed method, we first extract the review records of products to a temporal feature vector and then develop an isolation forest algorithm to detect outlier reviews by focusing on the differences between the patterns of product reviews to identify outlier reviews. We will verify the effectiveness of our method and compare it to some existing temporal outlier detection methods using the crawled Amazon China dataset. We will also study the impact caused by the parameter selection of the review records. Our work provides a new perspective of outlier review detection and our experiment demonstrates the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Blockchain performance cannot meet the requirement nowadays. One of the crucial ways to improve performance is sharding. However, most blockchain sharding research focuses on the public blockchain. As for consortium blockchain, previous studies cannot support high cross-shard efficiency, multiple-shard contract calling, strict transaction atomicity, and shard availability, which are essential requirements but also challenges in consortium blockchain systems. Facing these challenges, we propose Meepo, a systematic study on sharded consortium blockchain. Meepo enhances cross-shard efficiency via the cross-epoch and cross-call. Moreover, a partial cross-call merging strategy is designed to handle the multi-state dependency in contract calls, achieving flexible multiple-shard contract calling. Meepo employs a replay-epoch to ensure strict transaction atomicity, and it also uses a backup algorithm called shadow shard based recovery to improve the shard robustness. On a test-bed of 128 AliCloud servers, setting 32 shards and 4 consortium members, Meepo-OpenEtheruem can achieve more than 140,000 cross-shard TPS under the workload of 100,000,000 asset transactions. It also shows more than 50,000 TPS under the transactions of real-world shopping behaviors. [ABSTRACT FROM AUTHOR]

This paper describes a real case of redesigning a Chilean airline's website, considering cultural elements, customer behavior, and general user experience. We analyzed usability issues and problems in the information architecture, such as a benchmarking for best practices in the current area. We considered Hofstede's cultural recommendations, and previous work, to propose the final design. Different experiments and more than 40 direct participants worked in the current proposal. After a year of the implementation, and more than 4,000 transactions, the company obtained significant improvements and a better position on the market. [ABSTRACT FROM AUTHOR]

In the domain of e-commerce, acquiring a new customer is generally more expensive than keeping the existing ones. A successful prediction of churn of a specific customer provides an opportunity to change his/her decision to leave. In this paper, we propose a novel complex user model focused on the user churn intent prediction. The idea of our model is based on composing of multiple sets of features representing user's interaction with the web application. The performance of our model is evaluated indirectly by the prediction of churn, using real data from online retailers. The results show that the prediction using the proposed model outperforms the churn prediction based on baseline models across two domains. [ABSTRACT FROM AUTHOR]

E-commerce and other software-related projects are subject to disruptions that result in schedule delays, cost overruns, and degraded quality. Systems integration projects must address and mitigate the most disruptive scenarios resulting in the potential to miss predefined programmatic targets. In this paper, we present a method that aids in the selection of management actions that mitigate the effects of two concurrent modes of project disruption: 1) changes in the emphasis placed by decision-makers on project management success criteria, and 2) changes to the schedule network and its constituent activities. We identify disruptive emergent and future conditions and quantify those scenarios that are the most disruptive to the project. We then evaluate several management actions in mitigating project disruptions, accounting for benefits and costs. A systems integration of an e-commerce system with multiple functionalities is described in an example. [ABSTRACT FROM AUTHOR]

Content-based publish/subscribe systems have been employed to deal with complex distributed information flows in many applications. It is well recognized that event matching is a fundamental component of such large-scale systems. Event matching searches a space which is composed of all subscriptions. As the scale and complexity of a system grows, the efficiency of event matching becomes more critical to system performance. However, most existing methods suffer significant performance degradation when the system has large numbers of both subscriptions and their component constraints. In this paper, we present Hash Tree (H-Tree), a highly efficient index structure for event matching. H-Tree is a hash table in nature that is a combination of hash lists and hash chaining. A hash list is built up on an indexed attribute by realizing novel overlapping divisions of the attribute’s value domain, providing more efficient space consumption. Multiple hash lists are then combined into a hash tree. The basic idea behind H-Tree is that matching efficiencies are improved when the search space is substantially reduced by pruning most of the subscriptions that are not matched. We have implemented H-Tree and conducted extensive experiments in different settings. Experimental results demonstrate that H-Tree has better performance than its counterparts by a large margin. In particular, the matching speed is faster by three orders of magnitude than its counterparts when the numbers of both subscriptions and their component constraints are huge. [ABSTRACT FROM PUBLISHER]

This paper begins with a brief overview of how digital rights management (DRM) technologies are being used to limit consumer rights. Next we show how this code is combined with a legal component, including the use of contractual agreements, to further narrow user rights. Providing a summary of the legal history, we build up to the current legal environment surrounding DRM in the United States, showing the need for DRM policy and coding changes to better balance the interests of copyright holders and the public. [ABSTRACT FROM PUBLISHER]

The proliferation of e-commerce calls for mining consumer preferences and opinions from user-generated text. To this end, topic models have been widely adopted to discover the underlying semantic themes (i.e., topics). Supervised topic models have emerged to leverage discovered topics for predicting the response of interest (e.g., product quality and sales). However, supervised topic modeling remains a challenging problem because of the need to prespecify the number of topics, the lack of predictive information in topics, and limited scalability. In this paper, we propose a novel supervised topic model, Hierarchical Dirichlet Process-based Inverse Regression (HDP-IR). HDP-IR characterizes the corpus with a flexible number of topics, which prove to retain as much predictive information as the original corpus. Moreover, we develop an efficient inference algorithm capable of examining large-scale corpora (millions of documents or more). Three experiments were conducted to evaluate the predictive performance over major e-commerce benchmark testbeds of online reviews. Overall, HDP-IR outperformed existing state-of-the-art supervised topic models. Particularly, retaining sufficient predictive information improved predictive R-squared by over 17.6 percent; having topic structure flexibility contributed to predictive R-squared by at least 4.1 percent. HDP-IR provides an important step for future study on user-generated texts from a topic perspective. [ABSTRACT FROM PUBLISHER]

E-commerce business process nets (EBPNs) are a novel formal model for describing and validating e-commerce systems including interactive parties such as shopper, merchant, and third-party payment platform. Data errors and nondeterminacy of the data states during the trading process can be depicted with the help of EBPNs. However, the problem about how to analyze EBPNs remains largely open. To analyze their data-liveness, data-boundedness, and reachability, this paper presents two analysis methods. For EBPNs, reachability analysis is proposed based on a 3-D incidence matrix method. Additionally, reduction methods are proposed for a special EBPN. Finally, the validity and reliability of the proposed methods are illustrated via the examples of e-commerce systems. [ABSTRACT FROM PUBLISHER]

Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains. [ABSTRACT FROM PUBLISHER]

This paper investigates the difference in bullwhip effects in online and offline retail supply chains, offering insights into how frequent price discounts in e-commerce influence the bullwhip effect in the online retail supply chain. We consider a two-level online retail supply chain with a manufacturer and an online retailer in which the demand faced by the retailer is price sensitive and based on the price discount. Assuming that the online retailer employs an optimal order-up-to inventory policy with an optimal minimum mean-squared error forecasting technique, we derive the expression of the bullwhip effect in the online retail supply chain and make analysis and comparison. Finally, we develop a dual-channel supply chain model to directly observe the impact of price discounts in e-commerce on the bullwhip effect. The results suggest that price discounts in the online retail market generally amplify the bullwhip effect in the online retail supply chain, but in certain conditions, the bullwhip in the online supply chain may be smaller than that in the offline supply chain. We also find that the relationship between the lead time and the bullwhip effect in the online supply chain presents a distinctive feature contrary to the conclusions of previous studies. Based on the analysis, we develop important managerial insights regarding online retail supply chains. [ABSTRACT FROM AUTHOR]

Understanding what factors make a helpful online review is critical to increase sales and drive revenue for online retailers. This paper examined the impacts of both reviews' numerical and textual characteristics on review helpfulness across three different review types including comparative, suggestive, and regular reviews. With an analysis of 30 338 product reviews collected from http://Amazon.com, the results indicated that the effects of numerical characteristics of reviews on review helpfulness are stronger for regular reviews than those for suggestive and comparative reviews. The impacts of text sentiment on review helpfulness are more significant for suggestive and comparative reviews when compared with regular reviews. Moreover, the text complexity of reviews has a significant invert U-shaped relationship with review helpfulness, and the relationships are stronger for regular reviews when compared with suggestive and comparative reviews. Furthermore, text sentiment has a negative effect on review helpfulness, and the effect is stronger for suggestive reviews than that for comparative and regular reviews. Finally, we employ a random forest method to predict review helpfulness based on its numerical and textual characteristics. This paper found that review length is the most helpful factor in predicting the helpfulness of online reviews. Our findings also indicated that the importance of numerical characteristics is greater than that of textual characteristics across three different review types. The theoretical and practical implications of the findings are presented.

Technological advances and improved logistics efficiency, in conjunction with the widespread use of smartphones, have enabled retailers and technology companies to dramatically change Chinese consumers' purchasing behavior. This paper aims to discuss the current developments in online retail in China and to explore the associated challenges, particularly in regards to accounting. This paper adopts an exploratory case-study approach to investigate the marketing practice of online retail during the “Double 11” shopping festival in China. The results show that the adoption of new marketing practices in the Chinese online retail industry is likely to encourage sales; however, they are also introducing certain challenges, such as the need for collaboration between online and offline stores and the application of accounting standards, including recognition of revenue and categorization of insurance expenses. The findings of this paper have implications for both existing stakeholders and firms that are looking to adopt similar online retail practices.

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we propose learning both users’ and products’ feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework. [ABSTRACT FROM PUBLISHER]

Recently, online shopping integrating third-party payment platforms (TPPs) introduces new security challenges due to complex interactions between Application Programming Interfaces (APIs) of Merchants and TPPs. Malicious clients may exploit security vulnerabilities by calling APIs in an arbitrary order or playing various roles. To deal with the security issue in the early stages of system development, this paper presents a formal method for modeling and verification of online shopping business processes with malicious behavior patterns considered based on Petri nets. We propose a formal model called E-commerce Business Process Net to model a normal online shopping business process that represent intended functions, and malicious behavior patterns representing a potential attack that violates the security goals at the requirement analysis phase. Then, we synthesize the normal business process and malicious behavior patterns by an incremental modeling method. According to the synthetic model, we analyze whether an online shopping business process is resistant to the known malicious behavior patterns. As a result, our approach can make the software design provably secured from the malicious attacks at process design time and, thus, reduces the difficulty and cost of modification for imperfect systems at the release phase. We demonstrate our approach through a case study. [ABSTRACT FROM AUTHOR]

This paper presents an affective algorithm to support purchasing decisions in e-commerce environments. The literature associated with decision-making theory includes the affective dimension as a clear human decision factor. In the same sense, several models and proposals of consumer behavior consider both affective and social dimensions as very important factors that influence each human decision. However, the related work associated with electronic commerce proposes strongly the use of rational dimension as the main decision factor. Our approach proposes the incorporation of the affective dimension as a central component within an integrated decision schema. The proposed algorithm considers three different decision factors: an objective dimension, which represents the rational decision factors; an individual subjective dimension, which represents the affective decision factors; and a social subjective dimension, which represents the social decision factors. The study case explains how the proposal algorithm can be implemented in a real scenario. [ABSTRACT FROM PUBLISHER]

This paper focuses on the measure of recommendation stability, which reflects the consistency of recommender system predictions. Stability is a desired property of recommendation algorithms and has important implications on users’ trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms can suffer from a high degree of instability. In this study, we explore two scalable, general-purpose meta-algorithmic approaches—based on bagging and iterative smoothing—that can be used in conjunction with different traditional recommendation algorithms to improve their stability. Our experimental results on real-world rating data demonstrate that both approaches can achieve substantially higher stability as compared to the original recommendation algorithms. Furthermore, perhaps as importantly, the proposed approaches not only do not sacrifice the predictive accuracy in order to improve recommendation stability, but are actually able to provide additional accuracy improvements. [ABSTRACT FROM AUTHOR]

The “buy online and pick up in store” (BOPS) mode is gaining tremendous popularity among retailers since it is convenient for consumers and brings additional store sales to retailers. However, operating the BOPS channel requires additional investment, which is a challenge for retailers. This article considers the pricing strategies of competing dual-channel retailers, focuses on whether and when they should adopt the BOPS strategy, and explores the impacts of market factors on the equilibrium outcomes. Since retailers’ decisions are usually made sequentially in reality, we use the Stackelberg game model to analyze retailers’ optimal strategies. First, we show that the follower's price is not always lower than the leader's price. Specifically, when the unit additional profit from cross-selling of the follower is low enough, the follower will set a higher price than the leader. Second, we find that retailers prefer the BOPS strategy when the fixed costs for offering BOPS channels are low enough, or when the difference between the additional profits from cross-selling of two retailers is sufficiently large. Third, we present an interesting insight: an increase in product return probability or retailer cost of handling a returned product can be beneficial to retailers. [ABSTRACT FROM AUTHOR]

Rapidly escalating energy and cooling costs, especially those related to the energy consumption of storage systems, have become a concern for data centers, primarily because the amount of digital data that needs storage is increasing daily. In response, a multitude of energy saving approaches that take into account storage-device-level input/output (I/O) behaviors have been proposed. The trouble is that numerous critical applications such as database systems or web commerce applications are in constant operation at data centers, and the conventional approaches that only utilize storage-device-level I/O behaviors do not produce sufficient energy savings. It may be possible to dramatically reduce storage-related energy consumption without degrading application performance levels by utilizing application-level I/O behaviors. However, such behaviors differ from one application to another, and it would be too expensive to tailor methods to individual applications. As a way of solving this problem, we propose a universal storage energy management framework for runtime storage energy savings that can be applied to any type of application. The results of evaluations show that the use of this framework results in substantive energy savings compared with the traditional approaches that are used while applications are running. [ABSTRACT FROM AUTHOR]

Trust plays a crucial role in helping online users collect reliable information and it has gained increasing attention from the computer science community in recent years. Traditionally, research about online trust assumes static trust relations between users. However, trust, as a social concept, evolves as people interact. Most existing studies about trust evolution are from sociologists in the physical world while little work exists in an online world. Studying online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this work, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we identify the differences of trust evolution study in physical and online worlds and propose a framework, eTrust, to study trust evolution using online data from passive observation in the context of product review sites by exploiting the dynamics of user preferences. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as trust prediction, rating prediction and ranking evolution. [ABSTRACT FROM AUTHOR]

With the widespread adoption of social (aka Web 2.0) technologies like social networks, blogs, and wikis, there is a growing interest in looking into how enterprises should capitalize on these technologies. The social enterprise is the one that on top of having an online presence on the Internet, strives to open up new communication channels with stakeholders using social technologies. Building upon previous works on social business processes and on practical experiences on adopting such technologies in different Brazilian companies, this paper suggests an architecture and guiding framework for the social enterprise in terms of how to bridge the gap between the business world and social world and how to integrate social technologies into the enterprise's day-to-day operations. [ABSTRACT FROM PUBLISHER]

Social commerce, which is different from traditional e-commerce where people purchase products via initiative searching or recommendations from the platform, transforms a social community into an inclusive place to do business by enabling people to share products with their friends. A user (sharer), can share a link of a product to their social-connected friends (receiver). Once a receiver purchases the product, the sharer can earn commission provided by the platform. To promote sales, the platform can also assist sharers by providing product candidates which are more likely to be purchased during the social sharing. We define this task of generating sharing suggestions as item recommendation for word-of-mouth scenario, and to the best of our knowledge, this is a new task that has never been explored. In this article, we propose a TriM (short for Triad based word-of-Mouth recommendation) model that can capture both the sharer’s influence and the receiver’s interest at the same time, which are two significant factors that determine whether the receiver will buy the product or not. Furthermore, with joint learning on two parts of interaction data to address data sparsity issue, our proposed TriM-Joint further improves the recommendation performance. By conducting experiments, we show that our proposed models achieve the best results compared to state-of-the-art models with significant improvements by at least $7.4\% \sim 14.4\%$ 7. 4 % ∼ 14. 4 % respectively. [ABSTRACT FROM AUTHOR]

Video moderation, which refers to remove deviant or explicit content from e-commerce livestreams, has become prevalent owing to social and engaging features. However, this task is tedious and time consuming due to the difficulties associated with watching and reviewing multimodal video content, including video frames and audio clips. To ensure effective video moderation, we propose VideoModerator, a risk-aware framework that seamlessly integrates human knowledge with machine insights. This framework incorporates a set of advanced machine learning models to extract the risk-aware features from multimodal video content and discover potentially deviant videos. Moreover, this framework introduces an interactive visualization interface with three views, namely, a video view, a frame view, and an audio view. In the video view, we adopt a segmented timeline and highlight high-risk periods that may contain deviant information. In the frame view, we present a novel visual summarization method that combines risk-aware features and video context to enable quick video navigation. In the audio view, we employ a storyline-based design to provide a multi-faceted overview which can be used to explore audio content. Furthermore, we report the usage of VideoModerator through a case scenario and conduct experiments and a controlled user study to validate its effectiveness. [ABSTRACT FROM AUTHOR]

New advances in electronic commerce systems and communication technologies have made the credit card the potentially most popular method of payment for both regular and online purchases; thus, there is significantly increased fraud associated with such transactions. Fraudulent credit card transactions cost firms and consumers large financial losses every year, and fraudsters continuously attempt to find new technologies and methods for committing fraudulent transactions. The detection of fraudulent transactions has become a significant factor affecting the greater utilization of electronic payment. Thus, there is a need for efficient and effective approaches for detecting fraud in credit card transactions. This paper proposes an intelligent approach for detecting fraud in credit card transactions using an optimized light gradient boosting machine (OLightGBM). In the proposed approach, a Bayesian-based hyperparameter optimization algorithm is intelligently integrated to tune the parameters of a light gradient boosting machine (LightGBM). To demonstrate the effectiveness of our proposed OLightGBM for detecting fraud in credit card transactions, experiments were performed using two real-world public credit card transaction data sets consisting of fraudulent transactions and legitimate ones. Based on a comparison with other approaches using the two data sets, the proposed approach outperformed the other approaches and achieved the highest performance in terms of accuracy (98.40%), Area under receiver operating characteristic curve (AUC) (92.88%), Precision (97.34%) and F1-score (56.95%).

Since the existing machine-learning-based approaches for fraud detection are incapable of providing explanations, we propose a fraud detection method based on quantitative argumentation, which is intrinsically interpretable. First, we construct an argumentative tree by combining human-level knowledge and the knowledge learned from data. Second, we extend the existing quantitative argumentation debates (QuAD) frameworks by adding correlation strength between arguments and exploit the particle swarm optimization algorithm (PSO) to identify the correlation strength between arguments. Third, the performance of the new method is investigated by an empirical study, using the data from Ant Financial, the Alibaba Group's financial services provider. The results show that the new method has better performance than the existing DF-QuAD algorithm and is competitive with other machine learning methods, including Xgboost, ANN, SVM, and LR. [ABSTRACT FROM AUTHOR]

Retail shopping is being revolutionized by the Internet and e-commerce. Big data analytics, artificial intelligence, and Internet of Things devices have further fueled the transformation. [ABSTRACT FROM AUTHOR]

A total of 105 delegates from 26 countries representing about 60 different organizations met in Paris, France for the 16th ICIN conference to discuss new technologies from the Internet and from the telecommunications industry. ICIN 2015 took place February 17?19, with the technical co-sponsorship of the IEEE and IEEE Communications Society, and the support of Ericsson, Nokia, BroadSoft, Orange, and Institut Mines-Telecom. ICIN has a 26-year history of anticipating the key trends in communications networks and services, showcasing technologies and architectures that become vital elements of delivering services. [ABSTRACT FROM PUBLISHER]

Background: Consumer-to-consumer (C2C) e-commerce involves consumers re-selling products to other consumers using online platforms. Research identifies trust as a major factor in this exchange. It concludes that seller-generated product descriptions can mitigate mistrust. Further, technical and professional communication research can reveal what content sellers tend to provide and can reveal how platform design may encourage that content. Literature review: C2C e-commerce and TPC researchers agree that mistrust can be mitigated by detailed content, and they call for platform designers to help improve platform and seller reputations. Research questions: 1. What content do sellers provide about their technical products? 2. How do the platforms' web form designs and the associated documentation about listing a product for sale encourage certain content types? Research methodology: Four platforms were chosen using specific criteria. Product descriptions were collected once per week for six weeks, generating 1900 product descriptions. These descriptions were unitized and given reliable content categories, a methodology called quantitative content analysis. Further, the documentation and processes for posting items were explored to determine how they may encourage content types. Results/discussion: Sellers mostly provide product information and sales procedures, and they rarely give benefits and goodwill to the buyer. The platform design seems to encourage this content because of the content-entry process, the content-entry options, and the required and unrequired content entry. Conclusions: This study invites technical and professional communicators to provide more guidelines for users about the kinds of content they may include, and designers to explore the content entry process using usability and user-experience research. [ABSTRACT FROM AUTHOR]

Collaborative filtering is now successfully applied to recommender systems. The availability of extensive personal data is necessary for generating high quality recommendations. However, traditional collaborative filtering methods suffer from sparse and sometimes cold-start problems, particularly for newly deployed recommenders. Currently, several recommender systems exist in good working order, and data collected from these existing systems should be valuable for newly deployed recommenders. This paper introduces a privacy preserving shared collaborative filtering problem in order to leverage the data from other parties (contributors) to improve its own (beneficiaries) collaborative filtering performance, with the privacy protected under a differential privacy framework. It proposes a two-step methodology to solve this problem. First, item-based neighborhood information is selected as the shared data from the contributor with guaranteed differential privacy, and a practical enforcement mechanism for differential privacy is proposed. Second, two novel algorithms are developed to enable the beneficiary to leverage the shared data to support improved collaborative filtering. The extensive experimental results show that the proposed methodology can increase the recommendation accuracy of the beneficiary significantly while preserving data privacy for the contributors.

Presents corrections to author information from the paper, "Characterizing and predicting early reviewers for effective product marketing on e-commerce websites," (Bai, T., et al), IEEE Trans. Knowl. Data Eng., vol. 30, no. 12, pp. 2271–2284, Dec. 2018. [ABSTRACT FROM AUTHOR]

Scaling complex distributed systems such as e-commerce is an importance practice to simultaneously achieve high performance and high resource efficiency in the cloud. Most previous research focuses on hardware resource scaling to handle runtime workload variation. Through extensive experiments using a representative n-tier web application benchmark (RUBBoS), we demonstrate that scaling an n-tier system by adding or removing VMs without appropriately re-allocating soft resources (e.g., server threads and connections) may lead to significant performance degradation resulting from implicit change of request processing concurrency in the system, causing either over- or under-utilization of the critical hardware resource in the system. We build a concurrency-aware model that determines a near optimal soft resource allocation of each tier by combining some operational queuing laws and the fine-grained online measurement data of the system. We then develop a dynamic concurrency management (DCM) framework that integrates the concurrency-aware model to intelligently reallocate soft resources in the system during the system scaling process. We compare DCM with Amazon EC2-AutoScale, the state-of-the-art hardware only scaling management solution using six real-world bursty workload traces. The experimental results show that DCM achieves significantly shorter tail latency and higher throughput compared to Amazon EC2-AutoScale under all the workload traces. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of e-commerce, a growing number of customers choose to go shopping online. To find attractive products from online shopping marketplaces, the skyline query is a useful tool which offers more interesting and preferable choices for customers. The skyline query and its variants have been extensively investigated. However, to the best of our knowledge, they have not taken into account the requirements of customers in certain practical application scenarios. Recently, online shopping marketplaces usually hold some price promotion campaigns to attract customers and increase their purchase intention. Considering the requirements of customers in this practical application scenario, we are concerned about product selection under price promotion. We formulate a constrained optimal product combination (COPC) problem. It aims to find out the skyline product combinations which both meet a customer's willingness to pay and bring the maximum discount rate. The COPC problem is significant to offer powerful decision support for customers under price promotion, which is certified by a customer study. To process the COPC problem effectively, we first propose a two list exact (TLE) algorithm. The COPC problem is proven to be NP-hard, and the TLE algorithm is not scalable because it needs to process an exponential number of product combinations. Additionally, we design a lower bound approximate (LBA) algorithm that has a guarantee about the accuracy of the results and an incremental greedy (IG) algorithm that has good performance. The experiment results demonstrate the efficiency and effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Clustering of customer transaction data is an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we propose the “personalized product tree”, named purchase tree, to represent a customer’s transaction records. So the customers’ transaction data set can be compressed into a set of purchase trees. We propose a partitional clustering algorithm, named PurTreeClust, for fast clustering of purchase trees. A new distance metric is proposed to effectively compute the distance between two purchase trees. To cluster the purchase tree data, we first rank the purchase trees as candidate representative trees with a novel separate density, and then select the top $k$ customers as the representatives of $k$ customer groups. Finally, the clustering results are obtained by assigning each customer to the nearest representative. We also propose a gap statistic based method to evaluate the number of clusters. A series of experiments were conducted on ten real-life transaction data sets, and experimental results show the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]