The proliferation of coronavirus disease (COVID-19) has prompted changes in business models. To ensure a successful transition to non-face-to-face and electronic communication, the authenticity of data and the trustworthiness of communication partners are essential. Trust services provide a mechanism for preventing data falsification and spoofing. To develop a trust service, the characteristics of the service and the scope of its use need to be determined, and the relevant legal systems must be investigated. Preparing a document to meet trust service provider requirements may incur significant expenses. This study focuses on electronic signatures, proposes criteria for classification, classifies actual documents based on these criteria, and opens a discussion. A case study illustrates how trusted service providers search a document highlighting areas that require approval. The classification table in this paper may prove advantageous at the outset when business decisions are uncertain, and there is no clear starting point.

This paper addresses recommendation diversification. Existing diversification methods have difficulty in dealing with the tradeoff between accuracy and diversity. We point out the root of the problem in diversification methods and propose a novel method that can avoid the problem. Our method aims to find an optimal solution of the objective function that is carefully designed to consider user preference and the diversity among recommended items simultaneously. In addition, we propose an item clustering and a greedy approximation to achieve efficiency in recommendation.

Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

Purchase behavior prediction is one of the most important issues to promote both e-commerce companies' sales and the consumers' satisfaction. The prediction usually uses features based on the statistics of items. This kind of features can lead to the loss of detailed information of items. While all items are included, a large number of features has the negative impact on the efficiency of learning the predictive model. In this study, we propose to use the most popular items for improving the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. We also analyze the reason for the performance of the most popular items. In addition, our work also reveals if interactions among most popular items are taken into account, the further significant improvement can be achieved. One possible explanation is that online retailers usually use a variety of sales promotion methods and the interactions can help to predict the purchase behavior.

Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.

An electronic ticket is a contract, in digital format, between the user and the service provider, and reduces both economic costs and time in many services such as air travel industries or public transport. However, the security of the electronic ticket has to be strongly guaranteed, as well as the privacy of their users. We present an electronic ticketing system that considers these security requirements and includes the exculpability as a security requirement for these systems, i.e. users and the service provider can not falsely accuse each other of misbehavior. The system ensures that either both parties receive their desired data from other or neither does (fair exchange). Another interesting property is reusability. Thanks to reusability the tickets can be used a predefined number of times with the same security as single tickets. Furthermore, this scheme takes special care of the computational requirements on the users side by using light-weight cryptography. We show that the scheme is usable in practice by means of its implementation using mobile phones with Near Field Communication (NFC) capabilities.

Due to the exponentially increasing threat of cyber attacks, many e-commerce organizations around the world have begun to recognize the importance of information security. When considering the importance of security in e-commerce, we need to train e-commerce security experts who can help ensure the reliable deployment of e-commerce. The purpose of this research is to design and evaluate an e-commerce security curriculum useful in training e-commerce security experts. In this paper, we use a phase of the Delphi method and the Analytic Hierarchy Process (AHP) method. To validate our results, we divide the respondents into two groups and compare the survey results.

Cyberworlds are being formed in cyberspaces as computational spaces. Now cyberspaces are rapidly expanding on the Web either intentionally or spontaneously, with or without design. Widespread and intensive local activities are melting each other on the web globally to create cyberworlds. The major key players of cyberworlds include e-finance that trades a GDP-equivalent a day and e-manufacturing that is transforming industrial production into Web shopping of product components and assembly factories. Lacking proper theory and design, cyberworlds have continued to grow chaotic and are now out of human understanding and control. This research first presents a generic theoretical framework and design based on algebraic topology, and also provides an axiomatic approach to theorize the potentials of cyberworlds.

Recently, broadband access is widely spreading, and many broadband network E-commerce services are planned and developed. This article proposes a broadband online shop where a videoconferencing system is used to enable direct, face-to-face communication. It is important for a broadband online shop to understand what preference their customers want in order to provide them with more appropriate information. By using customer preferences, a salesclerk can have a serviceable conversation with few questions to his online customers. So, we are developing a visual Customer Relationship Management system (v-CRM system) that offers customer preferences to broadband network service such as broadband online shop. In this paper, we classify customer preferences, and describe three visualization methods that enable customer preferences to be intuitively understood quickly. We outline the v-CRM evaluation system and describe an experiment where we evaluated how accurately customer preferences can be recognized using these methods. The results show that v-CRM system is effective for understanding customer preferences.

Successful e-commerce presupposes techniques by which autonomous trading entities can interoperate. Although progress has been made on data exchange and payment protocols, interoperation in the face of autonomy is still inadequately understood. Current techniques, designed for closed environments, support only the simplest interactions. We develop a multiagent approach for interoperation of business process in e-commerce. This approach consists of (1) a behavioral model to specify autonomous, heterogeneous agents representing different trading entities (businesses, consumers, brokers), (2) a metamodel that provides a language (based on XML) for specifying a variety of service agreements and accommodating exceptions and revisions, and (3) an execution architecture that supports persistent and dynamic (re)execution.

This paper presents a formalism for the analysis of e-commerce protocols. The approach integrates logics and process calculi, providing an expressive message passing semantics and sophisticated constructs for modeling principals. A common set of inference rules for communication, reduction and information analysis supports proofs about message passing, the knowledge and behavior of principals, and protocol properties. The power of the formalism is illustrated with an analysis of the NetBill Protocol.