Social Relations and Methods in Recommender SystemsA Systematic Review

  1. Diego Medel 1
  2. Carina González-González 2
  3. Silvana V. Aciar 1
  1. 1 Universidad de San Juan (Argentina)
  2. 2 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2022

Volumen: 7

Número: 4

Páginas: 7-17

Tipo: Artículo

DOI: 10.9781/IJIMAI.2021.12.004 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

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.

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