Abstract
Nowadays, Movie constitutes 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 new movie rating recommendation approach, called MRRA, 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. Triadic concepts are exploited for providing important hidden correlations between rates, movies and users. Carried out experiments using a benchmark movie dataset revealed that the proposed movie rating recommendation approach outperforms conventional techniques.
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Notes
- 1.
MRRA is the acronym of Movie Rating Recommendation Approach.
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Trabelsi, C., Pasi, G. (2017). MRRA: A New Approach for Movie Rating Recommendation. In: Christiansen, H., Jaudoin, H., Chountas, P., Andreasen, T., Legind Larsen, H. (eds) Flexible Query Answering Systems. FQAS 2017. Lecture Notes in Computer Science(), vol 10333. Springer, Cham. https://doi.org/10.1007/978-3-319-59692-1_8
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