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Communication Dans Un Congrès Année : 2017

Exploring Current Viewing Context for TV Contents Recommendation

Résumé

Due to the diversity of alternative programs to watch and the change of viewers' contexts, real-time prediction of viewers' preferences in certain circumstances becomes increasingly hard. However, most existing TV recommender systems used only current time and location in a heuristic way and ignore other contextual information on which viewers' preferences may depend. This paper proposes a probabilistic approach that incorporates contextual information in order to predict the relevance of TV contents. We consider several viewer's current context elements and integrate them into a probabilistic model. We conduct a comprehensive effectiveness evaluation on a real dataset crawled from Pinhole platform. Experimental results demonstrate that our model outperforms the other context-aware models.
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Dates et versions

hal-02441970 , version 1 (16-01-2020)

Identifiants

  • HAL Id : hal-02441970 , version 1
  • OATAO : 24972

Citer

Meriam Bambia, Mohand Boughanem, Rim Faiz. Exploring Current Viewing Context for TV Contents Recommendation. IEEE/WIC/ACM International Conference on Web Intelligence (WI 2016), Oct 2016, Omaha, NE, United States. pp.272-279. ⟨hal-02441970⟩
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