SSSER: Spatiotemporal sequential and social embedding rank for successive point-of-interest recommendation

Abstract

Point-of-Interest (POI) recommendation is one of the important services of location-based social networks (LBSNs), which has become an important way to help users discover interesting places and increase the potential income of related companies. Although human movement presents a sequential pattern in the LBSN. There still are the following problems: (1) when modeling the sequence data, most of the existing works assume that the check-in time depends on the location transformation in the location sequence. In particular, these works emphasize the equivalent transition probabilities between locations for all users to capture the check-in sequential pattern, whereas they ignore the spatial and temporal information of personalized context in some actual personal check-in scenarios; (2) most of the existing POI recommendation algorithms fail to utilize the social information related to modeling users to improve the final recommendation performance.To tackle the above challenges, we propose a new personalized successive POI recommendation model called Spatiotemporal Sequential and Social Embedding Rank model, named SSSER. First, we use a hybrid deep learning model based on the convolution filter and multilayer perceptron model to mine the sequence pattern among the users' checked-in locations. Then, we use the method of metric learning to model the social relationship among users. Finally, we propose a unified framework to recommend POIs combining the users' personal interests, the check-in sequential influence and social information simultaneously for the successive POI recommendation. And the BPR standard is used to optimize the loss function to fit the user's partial order of POIs. The experimental results on the real datasets show that our proposed POI recommendation algorithm outperforms the other state-of-the-art POI recommendation algorithms.