Next POI Recommendation Method Based on Category Preference and Attention Mechanism in LBSNs

Focusing on learning the user’s behavioral characteristics during check-in activities, the next point of interest (POI) recommendation is to predict user’s destination to visit next. It is important for both the location-based service providers and users. Most of the existing studies have not taken full advantage of spatio-temporal information and user category preference, these are very important for analyzing user preference. Therefore, we propose a next POI recommendation algorithm named as CPAM that integrates category preference and attention mechanism to comprehensively structure user mobility patterns. We adopt the LSTM with multi-level attention mechanism to get user POI preference, which studies the weight of different contextual information of each check-in, and the different influence of each check-in the sequence to the next POI. In addition, we use LSTM to capture the user’s category transition preference to further improve the accuracy of recommendation. The experiment results on two real-world Foursquare datasets demonstrate that CPAM has better performance than the state-of-the art methods in terms of two commonly used metrics.