@inproceedings{4bc7c410b42c4ca385748bfca6cb16a2,
title = "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 clientside 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.",
keywords = "Online learning, Privacy, Recommender systems, Regret",
author = "Andr{\'e}s Moreno and Harold Castro and Michel Riveill",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 ; Conference date: 07-07-2014 Through 11-07-2014",
year = "2014",
doi = "10.1007/978-3-319-08786-3_33",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "369--380",
editor = "Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben",
booktitle = "User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings",
address = "Germany",
}