Next point-of-interest recommendation by sequential feature mining and public preference awareness

Abstract

With the widespread of location-based social networks (LBSNs), the amount of check-in data grows rapidly, which helps to recommend the next point-of-interest (POI). Extracting sequential patterns from check-in data has become a meaningful way for next POI recommendation, since human movement exhibits sequential patterns in LBSNs. However, due to the check-ins’ sparsity problem, exploiting sequential patterns in next POI recommendation is a challenging issue, which makes the learned sequential patterns unreliable. Inspired by the fact that auxiliary information can be incorporated to alleviate this situation, in this paper, we model sequential transition based on both item-wise check-in sequences and region-wise spatial information. Besides, we propose an attention-aware recurrent neural network (ATTRNN) to learn the contribution of different time steps. Furthermore, considering users’ decision-making is influenced by public’s common preference to some extent, we design a novel framework, namely HSP (short for “Hybrid model based on Sequential feature mining and Public preference awareness”), to recommend POIs for a given user. We conduct a comprehensive performance evaluation for HSP on two real-world datasets. Experimental results demonstrate that compared to other state-of-the-art techniques, the proposed HSP achieves significantly improvements.