How to exploit Recommender Systems in Social Media

PERSIA, Fabio, Mouzhi GE and Daniela D'AURIA. How to exploit Recommender Systems in Social Media. Online. In Proceedings of the IEEE 19th International Conference on Information Reuse and Integration for Data Science. Salt Lake City: IEEE, 2018, p. 537-541. ISBN 978-1-5386-2659-7. Available from: https://dx.doi.org/10.1109/IRI.2018.00085.

Basic information

• Original name: How to exploit Recommender Systems in Social Media
• Authors: PERSIA, Fabio (380 Italy), Mouzhi GE (156 China, guarantor, belonging to the institution) and Daniela D'AURIA.
• Edition: Salt Lake City, Proceedings of the IEEE 19th International Conference on Information Reuse and Integration for Data Science, p. 537-541, 5 pp. 2018.
• Publisher: IEEE

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• RIV identification code: RIV/00216224:14330/18:00103078
• Organization unit: Faculty of Informatics
• ISBN: 978-1-5386-2659-7
• Doi: http://dx.doi.org/10.1109/IRI.2018.00085
• UT WoS: 000442457000077
• Keywords in English: social media; recommender system; media recommendations; social media applications
• Tags: International impact, Reviewed

Abstract

The rapid increase and widespread of social media data have created new research challenges and opportunities for social media recommender systems, which are designed to recommend personalized, interesting, credible social media content with possible social impact. However, due to complexity in social network and new media interaction, the research of social media recommender systems is still on its initial stage. Therefore, this paper aims to review the state-of-the-art research that are related to social media recommender systems, and identify the critical factors for building new social media recommender systems. Our results show that relevance, validity, popularity, credibility and social impact are considered to be the 5 important factors for social media recommender systems.