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Hierarchical Reinforcement Learning for Point of Interest Recommendation
YANAN XIAO1,6; LU JIANG2; KUNPENG LIU3; YUANBO XU4,7; PENGYANG WANG5,8; MINGHAO YIN1,6
2024-08
Conference NameThe 33rd International Joint Conference on Artificial Intelligence (IJCAI-24)
Source PublicationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Pages2460-2468
Conference Date3-9 August 2024
Conference PlaceJeju, South Korea
PublisherInternational Joint Conferences on Artificial Intelligence
Abstract

With the increasing popularity of location-based services, accurately recommending points of interest (POIs) has become a critical task.Although existing technologies are proficient in processing sequential data, they fall short when it comes to accommodating the diversity and dynamism in users' POI selections, particularly in extracting key signals from complex historical behaviors.To address this challenge, we introduced the Hierarchical Reinforcement Learning Preprocessing Framework (HRL-PRP), a framework that can be integrated into existing recommendation models to effectively optimize user profiles.The HRL-PRP framework employs a two-tiered decision-making process, where the high-level process determines the necessity of modifying profiles, and the low-level process focuses on selecting POIs within the profiles.Through evaluations of multiple real-world datasets, we have demonstrated that HRL-PRP surpasses existing state-of-the-art methods in various recommendation performance metrics.

KeywordData Mining
DOI10.24963/ijcai.2024/272
Language英語English
Scopus ID2-s2.0-85204298637
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Citation statistics
Document TypeConference paper
CollectionFaculty of Science and Technology
THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPENGYANG WANG; MINGHAO YIN
Affiliation1.1School of Computer Science and Information Technology, Northeast Normal University
2.Department of Information Science and Technology, Dalian Maritime University
3.Department of Computer Science, Portland State University
4.College of Computer Science and Technology, Jilin University
5.Department of Computer and Information Science, University of Macau
6.Key Laboratory of Applied Statistics of MOE, Northeast Normal Universit
7.Mobile Intelligent Computing (MIC) Lab, Jilin University
8.The State Key Laboratory of Internet of Things for Smart City, University of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
YANAN XIAO,LU JIANG,KUNPENG LIU,et al. Hierarchical Reinforcement Learning for Point of Interest Recommendation[C]:International Joint Conferences on Artificial Intelligence, 2024, 2460-2468.
APA YANAN XIAO., LU JIANG., KUNPENG LIU., YUANBO XU., PENGYANG WANG., & MINGHAO YIN (2024). Hierarchical Reinforcement Learning for Point of Interest Recommendation. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2460-2468.
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