Abstract
Recommendation systems are currently widely used in domains where abundance of choice is conjoined with its subjective nature (books, movies, trips, etc.). Most of the modern recommendation systems are centralized. Although the centralized recommendation system design has some significant advantages, it also bears two primary disadvantages: the necessity for users to share their preferences and a single point of failure. This paper follows user-centric approach to distributed recommendation system design, and proposes an architecture of a collaborative peer-to-peer recommendation system with limited preferences’ disclosure. Privacy in the proposed design is provided by the fact that exact user preferences are never shared together with the user identity. To achieve that, the proposed architecture employs a locality-sensitive hashing of user preferences and an anonymized distributed hash table approach to peer-to-peer design.
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Acknowledgements
The research was partially supported by projects funded by grants # 13-07-00271, # 13-07-00039, and # 14-07-00345 of the Russian Foundation for Basic Research, project 213 (program 8) of the Presidium of the Russian Academy of Sciences, project # 2.2 of the basic research program “Intelligent information technologies, system analysis and automation” of the Nanotechnology and Information technology Department of the Russian Academy of Sciences. This work was partially financially supported by the Government of the Russian Federation, Grant 074-U01.
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Smirnov, A., Ponomarev, A. (2015). Locality-Sensitive Hashing for Distributed Privacy-Preserving Collaborative Filtering: An Approach and System Architecture. In: Hammoudi, S., Maciaszek, L., Teniente, E., Camp, O., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2015. Lecture Notes in Business Information Processing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-29133-8_22
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