深度学习在兴趣点推荐中的应用综述
Survey on Deep Learning Applications in Point-of-Interest Recommendation
摘要
Abstract
With the proliferation of mobile devices and location-based services,massive user check-in data from location-based social networks have generated widespread attention for point-of-interest(POI)recommendation as an important location service.Addressing challenges of data sparsity,complex spatiotemporal factors,dynamic user interest changes,privacy protection,and insufficient interpretability in traditional POI recommendation methods,this paper comprehensively reviews deep learning-based POI recommendation techniques.The formal definition of POI recommendation systems is introduced,and a general framework comprising data layer,feature engineering layer,deep learning model layer,and application layer is constructed.Deep learning techniques including recurrent neural networks,long short-term memory networks,gated recurrent units,attention mechanisms,transformers,and graph neural networks are systematically ana-lyzed for their application principles and core algorithms in POI recommendation.Mainstream dataset characteristics and evaluation metric applicability are thoroughly analyzed.Detailed classification and performance comparison are conducted for POI recommendation models based on sequence modeling,attention mechanisms,graph structures,multimodal fusion,and specific task orientations.Through practical application case analysis,the effectiveness of deep learning-driven POI recommendation systems is validated in tourist attraction recommendation,dining recommendation,urban service point recommendation,cross-city recommendation,and industrial-scale applications.Current research challenges are systemati-cally analyzed,including technical challenges,data challenges,and application challenges,covering key issues such as computational complexity and efficiency optimization,dynamic user preference modeling,interpretability and user trust,data sparsity and cold start problems,multimodal data fusion,privacy protection and fairness.Future development trends toward computational efficiency optimization,dynamic preference modeling,intrinsic interpretability,multimodal fusion,and privacy protection are prospected.关键词
兴趣点推荐/深度学习/图神经网络/注意力机制/时空建模/多模态融合Key words
point-of-interest recommendation/deep learning/graph neural network/attention mechanism/spatiotemporal modeling/multimodal fusion分类
信息技术与安全科学引用本文复制引用
黄屏,王峰,刘广腾,吴中博,李晓丽,黄金洲..深度学习在兴趣点推荐中的应用综述[J].计算机科学与探索,2026,20(3):671-710,40.基金项目
国家自然科学基金(62306108) （62306108）
湖北省自然科学基金创新发展联合基金(2022CFD101,2022CFD103) （2022CFD101,2022CFD103）
湖北省教育厅科学研究计划重点项目(D20192602) （D20192602）
襄阳市高新领域重点科技计划(2022ABH006848) （2022ABH006848）
湖北省高等学校优势特色学科群"新能源汽车与智慧交通"项目.This work was supported by the National Natural Science Foundation of China(62306108),the Hubei Provincial Natural Science Foun-dation Innovation and Development Joint Fund Project(2022CFD101,2022CFD103),the Key Project of the Scientific Research Pro-gram of Hubei Provincial Department of Education(D20192602),the Xiangyang High-Tech Key Science and Technology Program(2022ABH006848),and the Advantageous and Characteristic Discipline Group Program of Hubei Higher Education Institutions"New Energy Vehicles and Smart Transportation". （62306108）
