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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Moreno, A., Castro, H., Riveill, M. (2014). Client-Side Hybrid Rating Prediction for Recommendation. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_33
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DOI: https://doi.org/10.1007/978-3-319-08786-3_33
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