Next Location Recommendation: A Multi-context Features Integration Perspective

Next location recommendation aims to mine users’ historical trajectories to predict their potentially preferred locations in the next moment. Although previous studies have explored the idea of incorporating location or social contextual information for recommendation, they still suffer from several major limitations: (1) not fully considering the semantic associations between locations, (2) not considering the heterogeneity in preferences of socially linked users, (3) not fully utilizing contextual information from distinctive sources to further improve the recommendation performance. In this paper, we propose a novel multi-context-based next location recommendation model that incorporates location context, trajectory context, and social context to obtain comprehensive users’ preferences while allowing for interactions between contexts. Specifically, we first develop an efficient method combining both high-order location graphs and location semantic graphs to characterize subtle associations between locations. Then we explore the social contextual information and introduce the location subgraph which considers heterogeneous preferences among friends. Finally, we use the LSTM and geo-dilated LSTM to capture the spatio-temporal associations between users’ trajectories and integrate various contextual information to improve model performance. Extensive experiments on three real datasets show that our model has superior results in the next location recommendation task over other baselines.