Recommending Point-of-Interests with Real-Time Event Detection
Li Zhi1,2(),Sun Rui2,Yao Yuxuan1,3,Li Xiaohuan2
1School of Information Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha 410151, China 2Modern Applied Statistics and Big Data Research Center, Huaqiao University, Quanzhou 362021, China 3School of Information Science and Engineering, Hunan University, Changsha 410082, China
[Objective] This paper constructs a point-of-interest (POI) recommendation system based on real-time event detection, appropriate time and POI characteristics. [Methods] First, we retrieved the real-time events from a large number of tweets with geographical markers. Then, the system learned the embedded feature representation of real-time events and time perception information through tree convolution neural network. Third, we captured the perceptual features of POI’s graphic contents from comments and photos. Fourth, the system learned the graphic feature vector of POI with convolution neural network. Finally, we used the recall rate at the top K and the average of the reciprocal of the ranking to evaluate the effectiveness of different recommendation systems. [Results] The mean reciprocal rank (MRR) of the proposed model is 8.9% higher than that of the MP model and 57.9% higher than that of the non-negative matrix factorization (NMF) model. [Limitations] The characteristics of POI only include textual and image features, which need to be expanded. [Conclusions] The proposed model could effectively recommend point-of-interests, which benefits location-based services such as search, transportation and environmental monitoring.
李治, 孙锐, 姚羽轩, 李小欢. 基于实时事件侦测的兴趣点推荐系统研究*[J]. 数据分析与知识发现, 2022, 6(10): 114-127.
Li Zhi, Sun Rui, Yao Yuxuan, Li Xiaohuan. Recommending Point-of-Interests with Real-Time Event Detection. Data Analysis and Knowledge Discovery, 2022, 6(10): 114-127.
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