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Adaptive Successive POI Recommendation via trajectory sequences Processing and Long Short-Term Preference Learning

Computers, Materials & Continua 2024年第10期81卷 685-706页

作者： Yali Si Feng Li Shan Zhong Chenghang Huo Jing Chen Jinglian Liu School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China Suzhou Industrial Intelligence and Big Data Laboratory Suzhou215000China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang524088China

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning ***,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive *** better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal ***,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time ***,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active *** further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,*** experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation *** specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

关键词： Location-based social networks adaptive successive point-of-interest recommendation long short-term preference trajectory sequences

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