Social Relations and Methods in Recommender Systems: A Systematic Review

• Published in: International journal of interactive multimedia and artificial intelligence Vol. 7; no. 4; pp. 7 - 17
• Main Authors: Medel, Diego, Gonzalez-Gonzalez, Carina S, Aciar, Silvana V
• Format: Journal Article
• Language: English
• Published: IMAI Software 01.06.2022; Universidad Internacional de La Rioja (UNIR)
• Subjects: Analysis; Collaborative Filtering; recommendation systems; Recommender Systems; social; Social networks; Social Relationships; Systematic Review; Trust
• ISSN: 1989-1660; 1989-1660
• DOI: 10.9781/ijimai.2021.12.004

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations. Keywords Collaborative Filtering, Recommender Systems, Social Relationships, Systematic Review, Trust.