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Harnessing the Potential of HMM for Movie Rating Recommendation.
- Source :
- Procedia Computer Science; 2016, Vol. 96, p1543-1550, 8p
- Publication Year :
- 2016
-
Abstract
- The fast growing of on-line multimedia content have created the need to investigate new paradigms and techniques allowing to express how to index, retrieve and explore such contents. Indeed, nowadays, Movie becomes a predominant form of entertainment in human life. Most video websites such as YouTube and a number of social networks allow users to freely assign a rate to watched or bought videos or movies. In this paper, we introduce a movie rating recommendation approach based on the exploitation of the Hidden Markov Model (HMM). Specifically, we extend the HMM to include user's rating profiles, formally represented as triadic concepts. Carried out experiments emphasize the relevance of our proposal and open many thriving issues. [ABSTRACT FROM AUTHOR]
- Subjects :
- MOTION pictures & society
MOTION pictures
AMUSEMENTS
SOCIAL networks
PHYSIOLOGY
Subjects
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 96
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 117894618
- Full Text :
- https://doi.org/10.1016/j.procs.2016.08.201