Research Challenges in Multimedia Recommender Systems

GE, Mouzhi and Fabio PERSIA. Research Challenges in Multimedia Recommender Systems. Online. In Proceedings of the IEEE International Conference on Semantic Computing. San Diego, USA: IEEE, 2017, p. 344-347. ISBN 978-1-5090-4896-0. Available from: https://dx.doi.org/10.1109/ICSC.2017.31.

Basic information

• Original name: Research Challenges in Multimedia Recommender Systems
• Authors: GE, Mouzhi (156 China, guarantor, belonging to the institution) and Fabio PERSIA (380 Italy).
• Edition: San Diego, USA, Proceedings of the IEEE International Conference on Semantic Computing, p. 344-347, 4 pp. 2017.
• Publisher: IEEE

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: IEEE, CORE B Conference, SCOPUS, WoS, DBLP
• RIV identification code: RIV/00216224:14330/17:00096405
• Organization unit: Faculty of Informatics
• ISBN: 978-1-5090-4896-0
• Doi: http://dx.doi.org/10.1109/ICSC.2017.31
• UT WoS: 000403391300065
• Keywords in English: Multimedia Recommendation; Multimedia Recommender Systems; Research Challenges
• Tags: core_B, firank_B
• Tags: International impact, Reviewed

Abstract

Nowadays, since multimedia information has been extensively growing from a variety of sources, such photos from social networks, unstructured text from different websites, or raw video feed from digital sensors, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia, it is still challenging to provide effective recommendations, and research so far could only address limited aspects. Therefore, in this paper we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights to explain which aspects are worth further investigation.