Client-Side Hybrid Rating Prediction for Recommendation

  • Andrés Moreno
  • Harold Castro
  • Michel Riveill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8538)


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.


recommender systems privacy online learning regret 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrés Moreno
    • 1
    • 2
  • Harold Castro
    • 1
  • Michel Riveill
    • 2
  1. 1.School of EngineeringUniversidad de los AndesBogotáColombia
  2. 2.I3SUniversité de Nice Sophia AntipolisFrance

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