A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

Abstract

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets.