Social Relations and Methods in Recommender Systems: A Systematic Review

DIEGO MEDEL; CARINA GONZALEZ; SILVANA ACIAR

International Journal of Interactive Multimedia and Artificial Intelligence

UNIR

Año: 2022

With the constant growth of information, data sparsity problems, and cold start have become a complexproblem in obtaining accurate recommendations. Currently, authors consider the user´s historical behaviorand 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 socialrelationships between users was carried out. As the main findings, social relations were classified into threegroups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was themost used, and with the best results, considering the methods based on memory and model. The most usedmetrics that we found, and the recommendation methods studied in mobile applications are presented. Theinformation provided by this study can be valuable to increase the precision of the recommendations.