GE, Mouzhi and Fabio PERSIA. A Survey of Multimedia Recommender Systems: Challenges and Opportunities. International Journal of Semantic Computing. World Scientific Publishing, 2017, vol. 11, No 3, p. 411-428. ISSN 1793-351X. Available from: https://dx.doi.org/10.1142/S1793351X17500039.
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Basic information
Original name A Survey of Multimedia Recommender Systems: Challenges and Opportunities
Authors GE, Mouzhi (156 China, guarantor, belonging to the institution) and Fabio PERSIA.
Edition International Journal of Semantic Computing, World Scientific Publishing, 2017, 1793-351X.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14330/17:00096837
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1142/S1793351X17500039
UT WoS 000412119100009
Keywords in English Multimedia recommender system; multimedia objects; research challenges; multimedia recommendations
Tags International impact, Reviewed
Changed by Changed by: doc. Mouzhi Ge, Ph.D., učo 239833. Changed: 24/4/2018 10:45.
Abstract
Multimedia information has been extensively growing from a variety of sources such as cameras or video recorders. In order to select the useful multimedia objects, 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 objects, it is challenging to provide effective multimedia recommendations. In this paper, we therefore conduct a survey in both the multimedia information system and recommender system communities. We further focus on the works that span the two communities, especially the research on multimedia recommender systems. Based on our review, 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 of how to perform the follow-up research.
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