深度学习在兴趣点推荐中的应用综述OA

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.