MRRA: A New Approach for Movie Rating Recommendation

  • Chiraz Trabelsi
  • Gabriella Pasi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10333)


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.


User’s rating profile model Triadic analysis Rate recommendation Hidden Markov Model 


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculté des Sciences de Tunis, LIPAH-LR 11ES14Université de Tunis El ManarTunisTunisia
  2. 2.DISCo Viale SarcaUniversità degli Studi di Milano-BicoccaMilanoItaly

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