Exploiting crowd sourced reviews to explain movie recommendation

Sara El Aouad 1, 2, 3 Christophe Dupuy 4, 3 Renata Teixeira 1, 2 Christophe Diot 3 Francis Bach 4, 5
4 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Streaming services such as Netflix, M-Go, and Hulu use advanced recommender systems to help their customers identify relevant content quickly and easily. These recommenders display the list of recommended movies organized in sublists labeled with the genre or some more specific labels. Unfortunately , existing methods to extract these labeled sublists require human annotators to manually label movies, which is time-consuming and biased by the views of annotators. In this paper, we design a method that relies on crowd sourced reviews to automatically identify groups of similar movies and label these groups. Our method takes the content of movie reviews available online as input for an algorithm based on Latent Dirichlet Allocation (LDA) that identifies groups of similar movies. We separate the set of similar movies that share the same combination of genre in sublists and personalize the movies to show in each sublist using matrix factorization. The results of a side-by-side comparison of our method against Technicolor's M-Go VoD service are encouraging.
Type de document :
Communication dans un congrès
2nd Workshop on Recommendation Systems for TELEVISION and ONLINE VIDEO, Sep 2015, Vienna, Austria
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Contributeur : Sara El Aouad <>
Soumis le : mardi 22 septembre 2015 - 14:37:46
Dernière modification le : vendredi 31 août 2018 - 09:12:06
Document(s) archivé(s) le : lundi 28 décembre 2015 - 22:48:11


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  • HAL Id : hal-01193308, version 1



Sara El Aouad, Christophe Dupuy, Renata Teixeira, Christophe Diot, Francis Bach. Exploiting crowd sourced reviews to explain movie recommendation. 2nd Workshop on Recommendation Systems for TELEVISION and ONLINE VIDEO, Sep 2015, Vienna, Austria. 〈hal-01193308〉



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