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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (10): 114-127    DOI: 10.11925/infotech.2096-3467.2021.1461
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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
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Abstract  

[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.

Key wordsReal-Time Event      Deep Learning      Matrix Factorization      Convolutional Neural Network      Recommendation System     
Received: 28 December 2021      Published: 16 November 2022
ZTFLH:  TP391 G202  
Fund:Hunan Philosophy and Social Science Fund(21YBA282)
Corresponding Authors: Li Zhi,ORCID:0000-0001-5343-9699      E-mail: lizhicsp@sina.com

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1461     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I10/114

Example of Dependency Tree
System Architecture
Twitter Foursquare Instagram
用户数目(个) Check-ins数目(次) 平均Check-ins次数(次) 稀疏率 POIs数目(个) 图像数目(张)
原始 94 515 1 180 158 12.49 99.77% 5 278 464 359
处理后 12 684 50 675 4.00 98.68% 2 995 232 416
Data Filtering of Geo-tagged Tweets
Embedding Vectors of tree-CNN Extract Candidate Event Tweets
对比项目 NMF CDL ConvMF DCPR 本研究推荐模型
迭代次数 526 476 315 259 292
总计算时间/秒 24.4 28.2 19.3 25.5 23.2
The Iterations and the Total Calculation Time of Different Recommended Models
λq 0.001 0.01 0.1 1 10 100 1 000
R@5 0.21 0.27 0.29 0.32 0.28 0.23 0.17
MRR 0.132 0.164 0.193 0.213 0.202 0.178 0.147
The R@K (K=5) and the MRR of Different λq
λ 0.001 0.01 0.1 1 10 100 1 000
R@5 0.25 0.28 0.32 0.31 0.28 0.27 0.26
MRR 0.165 0.194 0.213 0.208 0.202 0.187 0.168
The R@K (K=5) and the MRR of Different λp
λv 0.001 0.01 0.1 1 10 100 1 000
R@5 0.28 0.31 0.32 0.31 0.28 0.26 0.23
MRR 0.166 0.199 0.215 0.204 0.192 0.183 0.172
The R@K (K=5) and the MRR of Different λv
指标 tree-CNN CNN LSTM
R@5 0.314 0.302 0.292
MRR 0.215 0.198 0.189
The R@K (K=5) and the MRR of Different Neural Network Models
Recommended Performance
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