Sequential Recommendation Based on Objective and Subjective Features

Nowadays, sequential recommender systems are widely used in E-commerce fields to capture consumers’ dynamic preferences in short terms. Existing transformer-based recommendation models mainly consider consumer preference for the products and some related features, such as price. However, besides such objective features, some subjective features, such as consumers’ preference for product quality, also affect consumers’ purchase decisions. In this paper, we design a Sequential Recommender system based on Objective and Subjective features (SROS). We construct subjective features by using natural language processing to analyze online consumer reviews. Then we design a feature-level multi-head self-attention to explore the interactions between objective features and subjective features and capture consumers’ dynamic preferences for them among different purchases. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems.

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach: The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings: Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications: The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value: This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches

The Mediation Effect of Trusting Beliefs on the Relationship Between Expectation-Confirmation and Satisfaction with the Usage of Online Product Recommendation

Online Product Recommendations (OPRs) are increasingly available to onlinecustomers as a value-added self-service in evaluating and choosing a product.Research has highlighted several advantages that customers can gain from usingOPRs. However, the realization of these advantages depends on whether and towhat extent customers embrace and fully utilise them. The relatively low OPR USAgerate indicates that customers have not yet developed trust in OPRs’ performance.Past studies also have established that satisfaction is a valid measure of systemperformance and a consistent significant determinant of users’ continuous systemusage. Therefore, this study aimed to examine the mediation effect of trustingbeliefs on the relationship between expectation-confirmation and satisfaction. Theproposed research model is tested using data collected via an online survey from626 existing users of OPRs. The empirical results revealed that social-psychologicalbeliefs (perceived confirmation and trust) are significant contributors to customersatisfaction with OPRs. Additionally, trusting beliefs partially mediate the impactof perceived confirmation on customer satisfaction. Moreover, this study validatesthe extensions of the interpersonal trust construct to trust in OPRs and examinesthe nomological validity of trust in terms of competence, benevolence, andintegrity. The findings provide a number of theoretical and practical implications.&nbsp

Recommendation, collaboration and social search

This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online