Aggregated Temporal Tensor Factorization Model for Point-of-interest Recommendation

Abstract

Temporal influence plays an important role in a point-of-interest (POI) recommendation system that suggests POIs for users in location-based social networks (LBSNs). Previous studies observe that the user mobility in LBSNs exhibits distinct temporal features, summarized as periodicity, consecutiveness, and non-uniformness. By capturing the observed temporal features, a variety of systems are proposed to enhance POI recommendation. However, previous work does not model the three features together. More importantly, we observe that the temporal influence exists at different time scales, yet this observation cannot be modeled in prior work. In this paper, we propose an Aggregated Temporal Tensor Factorization (ATTF) model for POI recommendation to capture the three temporal features together, as well as at different time scales. Specifically, we employ temporal tensor factorization to model the check-in activity, subsuming the three temporal features together. Furthermore, we exploit a linear combination operator to aggregate temporal latent features’ contributions at different time scales. Experiments on two real life datasets show that the ATTF model achieves better performance than models capturing temporal influence at single scale. In addition, our proposed ATTF model outperforms the state-of-the-art methods.