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arXiv · 2107.06590

Self-Determined Reciprocal Recommender System with Strong Privacy Guarantees

S. Nuñez von Voigt·E. Daniel·F. Tschorsch
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Abstract

Recommender systems are widely used. Usually, recommender systems are based on a centralized client-server architecture. However, this approach implies drawbacks regarding the privacy of users. In this paper, we propose a distributed reciprocal recommender system with strong, self-determined privacy guarantees, i.e., local differential privacy. More precisely, users randomize their profiles locally and exchange them via a peer-to-peer network. Recommendations are then computed and ranked locally by estimating similarities between profiles. We evaluate recommendation accuracy of a job recommender system and demonstrate that our method provides acceptable utility under strong privacy requirements.

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S. Nuñez von Voigt, E. Daniel, F. Tschorsch. 2021-07-14. Self-Determined Reciprocal Recommender System with Strong Privacy Guarantees. https://doi.org/10.1145/3465481.3465769

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