Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

作     者：Xi SUN Zhimin LV Xi SUN;Zhimin LV

作者机构：Collaborative Innovation Center of Steel TechnologyUniversity of Science and Technology BeijingBeijing 100083China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2023年第24卷第9期

页      面：1273-1286页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Point-of-interest recommendation Spatiotemporal effects Long short-term memory(LSTM) Attention mechanism 

摘      要：Next point-of-interest(POI)recommendation is an important personalized task in location-based social networks(LBSNs)and aims to recommend the next POI for users in a specific situation with historical check-in ***-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network(RNN)based models for ***,these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate *** address these limitations,a spatiotemporal trajectory(STT)model is proposed in this *** use the long short-term memory(LSTM)model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in *** the process of encoding information,an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and *** addition,we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate *** evaluate the performance of our STT model with four real-world *** results show that our model outperforms existing state-of-the-art methods.