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Privacy-enabled scalable recommender systems

Andrés Dario Moreno Barbosa 1
1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe RAINBOW
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : The main objective of this thesis is to propose a recommendation method that keeps in mind the privacy of users as well as the scalability of the system. To achieve this goal, an hybrid technique using content-based and collaborative filtering paradigms is used in order to attain an accurate model for recommendation, under the strain of mechanisms designed to keep user privacy, particularly designed to reduce the user exposure risk. The thesis contributions are threefold : First, a Collaborative Filtering model is defined by using client-side agent that interacts with public information about items kept on the recommender system side. Later, this model is extended into an hybrid approach for recommendation that includes a content-based strategy for content recommendation. Using a knowledge model based on keywords that describe the item domain, the hybrid approach increases the predictive performance of the models without much computational effort on the cold-start setting. Finally, some strategies to improve the recommender system's provided privacy are introduced: Random noise generation is used to limit the possible inferences an attacker can make when continually observing the interaction between the client-side agent and the server, and a blacklisted strategy is used to refrain the server from learning interactions that the user considers violate her privacy. The use of the hybrid model mitigates the negative impact these strategies cause on the predictive performance of the recommendations.
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Andrés Dario Moreno Barbosa. Privacy-enabled scalable recommender systems. Other [cs.OH]. Université Nice Sophia Antipolis, 2014. English. ⟨NNT : 2014NICE4128⟩. ⟨tel-01135312⟩

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