Advanced Recommender Systems by Exploiting Social Networks

GE, Mouzhi, Fabio PERSIA a Daniela D'AURIA. Advanced Recommender Systems by Exploiting Social Networks. In Proceedings of the IEEE International Conference on Humanized Computing and Communication. Laguna Hills, CA, USA: IEEE. s. 118-125. ISBN 978-1-72814-125-1. doi:10.1109/HCC46620.2019.00025. 2019.

Základní údaje

• Originální název: Advanced Recommender Systems by Exploiting Social Networks
• Autoři: GE, Mouzhi (156 Čína, garant, domácí), Fabio PERSIA (380 Itálie) a Daniela D'AURIA (380 Itálie).
• Vydání: Laguna Hills, CA, USA, Proceedings of the IEEE International Conference on Humanized Computing and Communication, od s. 118-125, 8 s. 2019.
• Nakladatel: IEEE

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Stať ve sborníku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Spojené státy
• Utajení: není předmětem státního či obchodního tajemství
• Forma vydání: tištěná verze "print"
• Kód RIV: RIV/00216224:14330/19:00112116
• Organizační jednotka: Fakulta informatiky
• ISBN: 978-1-72814-125-1
• Doi: http://dx.doi.org/10.1109/HCC46620.2019.00025
• UT WoS: 000525609700017
• Klíčová slova anglicky: Recommender systems;Social media;Social networks
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

Social networks have become an indispensable part of our lives, which serve as communication channels, social interaction platforms as well as ubiquitous entertainment tools; meanwhile, social networks constantly generate big social media data that create decision complexity and information overload to users. As a result, recommender systems are emerged to suggest personalized and possibly preferred media for the users. However, social networks have extensively enriched the inputs for recommender systems, such as users' social relations, data source credibility, and new social media types. Consequently, this paper is aimed at identifying the crucial factors that can be used to advance recommender systems in social networks. For each factor, this paper discusses the state-of-the-art recommender system research in that aspect, and suggests how to integrate the featured data to build and improve recommender systems for social networks. The paper further proposes a model to integrate the crucial factors and indicates possible application domains for social media recommender systems.