Extreme Learning Machine Based Point-of-Interest Recommendation in Location-Based Social Networks

Abstract

Researches on Point-of-Interests (POIs) have attracted a lot of attentions in Location-based Social Networks (LBSNs) in recent years. Existing studies on this topic most treat this kind of recommendation as just a type of point recommendation according to its similar properties for collaborative filtering. We argue that this recommending strategy could yield inaccuracy because these properties could not illustrate complete information of POIs for users. In this paper, we propose a novel Extreme Learning Machine (ELM) based approach named ELM Based POI Recommendation (EPR), which takes into account user preference, periodical movement and social relationship to discover the correlation of a user and a certain POI. Furthermore, we model recommendation in EPR as the problem of binary-class classification for each individual user and POI pair. To our best knowledge, this is the first work on POI recommendation in LBSNs by exploring the preference property, social property and periodicity property simultaneously. We show through comprehensive evaluation that the proposed approach delivers excellent performance and outperforms existing state-of-the-art POI recommendation methods, especially for cold start users.