Survey of Point-of-Interest Recommendation Research Fused with Deep Learning
摘要
Point-of-interest (POI) recommendation has emerged as a focal point in the research of location-based social network (LBSN) in recent years. It can help users find their favorite venue and bring considerable benefits to businesses. Nowadays, deep learning is gradually applied to the task of recommendation system because it can capture the nonlinear relationship between users and items more effectively. This paper thus focuses on recent research on POI recommendation combined with deep learning. Firstly, we introduce the difference between POI recommendation and other traditional recommendation tasks and illustrate various influencing factors that can improve the performance of the model. Then, the methods of applying deep learning to POI recommendation are divided into four categories, including POI embedding, deep collaborative filtering, feature extraction from side information, and sequence recommendation using recurrent neural network (RNN). We also investigate the development of user models performance and advantages combined with deep learning in these different aspects of applications. Finally, we summarize and look forward to the development of POI recommendation research combined with deep learning.