Social Relations and Methods in Recommender Systems: A Systematic Review

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Keywords
Abstract
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.
Year of Publication
2022
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
7
Issue
Regular Issue
Number
4
Number of Pages
7-17
Date Published
06/2022
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_1.pdf
DOI
10.9781/ijimai.2021.12.004
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