A social recommendation method based on the integration of social relationship and product popularity. Issue 121 (January 2019)

• Record Type: Journal Article
• Authors: Lai, Chin-Hui; Lee, Shin-Jye; Huang, Hung-Ling
• Is Part Of: International journal of human-computer studies. Issue 121(2019)
• Page Start: 42
• Page End: 57
• Publication Date: 2019-01
• Subjects: Social network -- Social interaction -- Trust relationship -- Recommender System -- Collaborative Filtering; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2018.04.002
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: We propose a social recommendation method based on integration of interaction, trust relationships and product popularity. The proposed method focuses on the analysis of users' interaction behavior to infer users' latent interaction relationships. Users' co-rated items and the role importance on a social networks are used to infer their implicit trust relationship. The proposed method aims to accurately analyze users' preferences in social networks for improving the prediction accuracy. The proposed method can accurately predict users' preference and support users' purchase decision making. Abstract: Web 2.0 technology fosters the flourishing growth and development of social networks. More and more people are participating in the activities on social networks to interact and share information with each other. Thus, consumers are often making their purchasing decisions based on information from the Internet such as reviews, ratings, and comments on products, especially from their trusted friends. However, a great amount of available information may cause the problem of information overload for consumers. In seeking to attain a good recommendation performance by taking the high-potential factors into account as far as possible, this paper proposes a novel social recommendation method on the basis of the integration of interactions, trust relationships and product popularity to predict user preferences, and recommend relevant products in social networks. In addition, the proposed method mainly focuses on analyzing user interactions to infer their latent interactions in accordance with the user ratings and corresponding reviews. Additionally, users may be affected by the popularity of products, so this factor has also been taken into consideration in this work. The experimental results show that the proposed recommendation method has a better recommendation performance in comparisons to other methods because the proposed method can accurately analyze user preferences and further recommend high-potential products to target users in social networks to support their purchase decision making. Furthermore, the proposed method can not only reduce the time and effort users spend on querying information, but also positively relieve the problem of information overload. Graphical abstract: