01612nas a2200241   4500000000100000000000100001008004100002260001200043653002800055653002700083653001100110653002200121653001000143100001600153700003100169700002100200245007700221856008000298300000900378490000600387520096300393022001401356       2022                            d  c06/202210aCollaborative Filtering10aRecommendation Systems10aSocial10aSystematic Review10aTrust1 aDiego Medel1 aCarina González-González1 aSilvana V. Aciar00aSocial Relations and Methods in Recommender Systems: A Systematic Review  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_1.pdf  a7-170 v73 aWith 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.  a1989-1660