A hybrid peer-to-peer recommendation system architecture based on locality-sensitive hashing

Recommendation systems have become ubiquitous recently as they help to mitigate information overflow of the nowadays life. The vast majority of current recommendation system approaches are centralized. Although centralized recommendations have several significant advantages, they also have two main drawbacks: single point of failure and the necessity for users to share their preferences. In this paper, a system architecture of a peer-to-peer recommendation system with limited preferences disclosure is proposed. The proposed architecture is based on a locality-sensitive hashing of user preferences and an anonymized distributed hash table approach to peer-to-peer design

Stochastically robust personalized ranking for LSH recommendation retrieval

National Research Foundation (NRF) Singapore under NRF Fellowship Programm