Recommendation Method for Time-Sequence Point of Interest via Spatio-Temporal Vicinity Perception

Abstract

How to capture the dynamic changes and dependencies of user behavior is a vital issue existing in point-of-interest (POI) recommendation. It mainly faces challenges including data scarcity, difficulty in extracting spatio-temporal sequence features and in capturing users?? individuated differences. In order to address these challenges, this paper proposes a time-sequence POI recommendation method based on spatio-temporal vicinity perception and implicit changes of users?? state. This method is aimed at converting the learning of user behavior into the learning of users?? latent state, combined with distance information to introduce spatial information, which effectively captures users?? mobile characteristics. Firstly, the variational autoencoder is utilized to represent the potential state of users. And then the dependency among the latent states is learnt through the graph neural network so as to capture the time-sequence dependence of user behavior. Furthermore, this paper makes use of the attention mechanism and radial basis function to capture the spatial dependence between the user and location candidate sets. Next, this paper evaluates the frequencies of user visiting each location, hence achieving point-of-interest recommendation. Experimental comparison and analysis on three real datasets demonstrate that the temporal recommendation performance of the proposed method is superior to existing benchmark algorithms.

Keywords

• point-of-interest (poi) recommendation; variational auto-encoder; graph neural network; attention mechanism