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 be-tween 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 da-taset,we achieve improvements ranging from 1.52％ to 24.95％ .On the Instagram dataset,the improve-ments 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 spa-tial distance and temporal interval factors,thereby improving the accuracy of POI recommendation.
关键词
兴趣点推荐/空间距离/时间间隔/门控扩张残差/时空序列
Key words
point of interest recommendation/spatial distance/time interval/gated dilation residual/spatiotemporal sequence
维普
万方数据