Harnessing the Potential of HMM for Movie Rating Recommendation

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