Client-Side Hybrid Rating Prediction for Recommendation

Abstract : The centralized gathering and processing of user information made by traditional recommender systems can lead to user information exposure, violating her privacy. Client-side personalization methods have been created as a mean for avoiding privacy risks. Motivated by limiting the exposure of user private information, we explore the use of a client-side hybrid recommender system placed on the online learning setting. We propose a prediction model based on an ensemble blender of an online matrix factorization CF model and a logistic regression model trained on item metadata with a probabilistic feature inclusion strategy. The final prediction is a blend of the two models on a weighted regret approach. We validate our approach with the Movielens 10M dataset.
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Communication dans un congrès
Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben. 22nd International Conference on User Modeling, Adaptation, and Personalization (UMAP 2014), Jul 2014, Aalborg, Denmark. Springer International Publishing, Lecture Notes in Computer Science, 8538, pp.369-380, 2014, Lecture Notes in Computer Science. <10.1007/978-3-319-08786-3_33>
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https://hal.archives-ouvertes.fr/hal-01342076
Contributeur : Michel Riveill <>
Soumis le : mardi 5 juillet 2016 - 13:39:45
Dernière modification le : mardi 7 mars 2017 - 01:09:03

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Andrés Moreno, Harold Castro, Michel Riveill. Client-Side Hybrid Rating Prediction for Recommendation. Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben. 22nd International Conference on User Modeling, Adaptation, and Personalization (UMAP 2014), Jul 2014, Aalborg, Denmark. Springer International Publishing, Lecture Notes in Computer Science, 8538, pp.369-380, 2014, Lecture Notes in Computer Science. <10.1007/978-3-319-08786-3_33>. <hal-01342076>

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