Abstract:
Objectives Personalized point of interest (POI) recommendation is a vital service in location-based social network. It can effectively use the sequence and spatiotemporal context information of check-in data to discover movement patterns and preferences of users.
Methods This paper proposes a probabilistic generative model with embedded spatiotemporal conditions to fully exploit the long-term dependency between personalized spatiotemporal preferences and sequential check-in sequences of users, constructs a gated dilation residual network, and implements a POI recommendation method based on gated dilation residual network. The method in this paper learns check-in sequences of users through a gated dilation residual network. It mines and captures the spatiotemporal patterns, sequence preferences and temporal preferences constrained by the spatial distance and time interval of sequential check-in behavior of users.
Results The proposed method shows significant improvements on the Foursquare and Instagram datasets. Compared to the best-performing algorithm NextItNet, our method demonstrates noticeable enhancements in terms of recall, precision, F1 score, and normalized discounted cumulative gain. On the Foursquare dataset, we achieve improvements ranging from 1.52% to 24.95%. On the Instagram dataset, the improvements range from 7.06% to 42.47%.
Conclusions The proposed method is more suitable for mining the long-term dependency relationships in sequential check-in behavior of users. It effectively incorporates spatial distance and temporal interval factors, thereby improving the accuracy of POI recommendation.
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