Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation

Abstract

Point-of-interest (POI) recommendation that suggests new locations for people to visit is an important application in location-based social networks (LBSNs). Compared with traditional recommendation problems, e.g., movie recommendation, geographical influence is a special feature that plays an important role in recommending POIs. Various methods that incorporate geographical influence into collaborative filtering techniques have recently been proposed for POI recommendation. However, previous geographical models have struggled with a problem of geographically noisy POIs, defined as POIs that follow the geographical influence but do not satisfy users’ preferences. We observe that users in the same geographical region share many POIs, and thus we propose the co-geographical influence to filter geographically noisy POIs. Furthermore, we propose the Geo-Pairwise Ranking Matrix Factorization (Geo-PRMF) model for POI recommendation, which incorporates co-geographical influence into a personalized pairwise preference ranking matrix factorization model. We conduct experiments on two real-life datasets, i.e., Foursquare and Gowalla, and the experimental results reveal that the proposed approach outperforms state-of-the-art models.