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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 words： Real-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
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Zhi Li
Rui Sun
Yuxuan Yao
Xiaohuan Li

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
[1] Wang H L, Li P Y, Liu Y, et al. Towards Real-Time Demand-Aware Sequential POI Recommendation[J]. Information Sciences, 2021, 547: 482-497.
doi: 10.1016/j.ins.2020.08.088
[2] Chen M, Li W Z, Qian L, et al. Next POI Recommendation Based on Location Interest Mining with Recurrent Neural Networks[J]. Journal of Computer Science and Technology, 2020, 35(3): 603-616.
doi: 10.1007/s11390-020-9107-3
[3] Yuan C Z, Bao Z F, Sanderson M, et al. Incorporating Word Attention with Convolutional Neural Networks for Abstractive Summarization[J]. World Wide Web, 2020, 23(1): 267-287.
doi: 10.1007/s11280-019-00709-6
[4] Li D, Liu H, Zhang Z L, et al. CARM: Confidence-Aware Recommender Model via Review Representation Learning and Historical Rating Behavior in the Online Platforms[J]. Neurocomputing, 2021, 455: 283-296.
doi: 10.1016/j.neucom.2021.03.122
[5] 丁浩, 艾文华, 胡广伟, 等. 融合用户兴趣波动时序的个性化推荐模型[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[5] (Ding Hao, Ai Wenhua, Hu Guangwei, et al. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. Data Analysis and Knowledge Discovery, 2021, 5(11): 45-58.)
[6] Suo X H, Guo B, Shen Y, et al. Embodying the Number of an Entity’s Relations for Knowledge Representation Learning[J]. International Journal of Software Engineering and Knowledge Engineering, 2021, 31(10): 1495-1515.
doi: 10.1142/S0218194021500509
[7] Park D, Kim S, Lee J, et al. ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 361-370.
doi: 10.1109/TVCG.2017.2744478
[8] Labani M, Moradi P, Ahmadizar F, et al. A Novel Multivariate Filter Method for Feature Selection in Text Classification Problems[J]. Engineering Applications of Artificial Intelligence, 2018, 70: 25-37.
doi: 10.1016/j.engappai.2017.12.014
[9] 陈欣, 杨小兵, 姚雨虹. 字词融合的双通道混合神经网络情感分析模型[J]. 小型微型计算机系统, 2021, 42(2): 279-284.
[9] (Chen Xin, Yang Xiaobing, Yao Yuhong. Two-Channel Mixed Neural Network Sentiment Analysis Model Based on Character and Word Fusion[J]. Journal of Chinese Computer Systems, 2021, 42(2): 279-284.)
[10] Wu Y, Ji Q. Discriminative Deep Face Shape Model for Facial Point Detection[J]. International Journal of Computer Vision, 2015, 113(1): 37-53.
doi: 10.1007/s11263-014-0775-8
[11] Sainath T N, Kingsbury B, Saon G, et al. Deep Convolutional Neural Networks for Large-Scale Speech Tasks[J]. Neural Networks, 2015, 64: 39-48.
doi: 10.1016/j.neunet.2014.08.005 pmid: 25439765
[12] Gonzalez R C. Deep Convolutional Neural Networks [Lecture Notes][J]. IEEE Signal Processing Magazine, 2018, 35(6): 79-87.
doi: 10.1109/MSP.2018.2842646
[13] Lou R, Lalevic D, Chambers C, et al. Automated Detection of Radiology Reports That Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification[J]. Journal of Digital Imaging, 2020, 33(1): 131-136.
doi: 10.1007/s10278-019-00271-7 pmid: 31482317
[14] Ackerman B, Wang C, Chen Y. A Session-Specific Opportunity Cost Model for Rank-Oriented Recommendation[J]. Journal of the Association for Information Science and Technology, 2018, 69(10): 1259-1270.
doi: 10.1002/asi.24044
[15] 鲜学丰, 陈晓杰, 赵朋朋, 等. 基于上下文感知和个性化度量嵌入的下一个兴趣点推荐[J]. 计算机工程与科学, 2018, 40(4): 616-625.
[15] (Xian Xuefeng, Chen Xiaojie, Zhao Pengpeng, et al. Context-Aware Personalized Metric Embedding for next POI Recommendation[J]. Computer Engineering & Science, 2018, 40(4): 616-625.)
[16] Lu Y S, Huang J L. GLR: A Graph-Based Latent Representation Model for Successive POI Recommendation[J]. Future Generation Computer Systems, 2020, 102: 230-244.
doi: 10.1016/j.future.2019.07.074
[17] Bao Y, Huang Z, Li L N, et al. A BiLSTM-CNN Model for Predicting Users’ Next Locations Based on Geotagged Social Media[J]. International Journal of Geographical Information Science, 2021, 35(4): 639-660.
doi: 10.1080/13658816.2020.1808896
[18] Liu W, Lai H J, Wang J, et al. Mix Geographical Information into Local Collaborative Ranking for POI Recommendation[J]. World Wide Web, 2020, 23(1): 131-152.
doi: 10.1007/s11280-019-00681-1
[19] Yan D F, Zhao X, Guo Z K. Personalized POI Recommendation Based on Subway Network Features and Users’ Historical Behaviors[J]. Wireless Communications and Mobile Computing, 2018, 2018: 1-10.
[20] Zhu J H, Wang C, Guo X, et al. Friend and POI Recommendation Based on Social Trust Cluster in Location-Based Social Networks[J]. EURASIP Journal on Wireless Communications and Networking, 2019, 2019: 89.
doi: 10.1186/s13638-019-1388-2
[21] Lai C H, Lee S J, Huang H L. A Social Recommendation Method Based on the Integration of Social Relationship and Product Popularity[J]. International Journal of Human-Computer Studies, 2019, 121: 42-57.
doi: 10.1016/j.ijhcs.2018.04.002
[22] Pliakos K, Joo S H, Park J Y, et al. Integrating Machine Learning into Item Response Theory for Addressing the Cold Start Problem in Adaptive Learning Systems[J]. Computers & Education, 2019, 137: 91-103.
doi: 10.1016/j.compedu.2019.04.009
[23] Wang S H, Wang Y L, Tang J L, et al. What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation[C]// Proceedings of the 26th International Conference on World Wide Web. 2017: 391-400.
[24] Yang D, Zhang D, Yu Z, et al. A Sentiment-Enhanced Personalized Location Recommendation System[C]// Proceedings of the 24th ACM Conference on Hypertext and Social Media. 2013: 119-128.
[25] Xing S N, Liu F A, Wang Q Q, et al. Content-Aware Point-of-Interest Recommendation Based on Convolutional Neural Network[J]. Applied Intelligence, 2019, 49(3): 858-871.
doi: 10.1007/s10489-018-1276-1
[26] Zhang Z B, Zou C, Ding R F, et al. VCG: Exploiting Visual Contents and Geographical Influence for Point-of-Interest Recommendation[J]. Neurocomputing, 2019, 357: 53-65.
doi: 10.1016/j.neucom.2019.04.079
[27] Khan N U, Wan W G, Yu S, et al. A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data[J]. ISPRS International Journal of Geo-Information, 2020, 9(12): 733.
doi: 10.3390/ijgi9120733
[27] (Zhang C, Lei D, Yuan Q, et al. GeoBurst+: Effective and Real-Time Local Event Detection in Geo-Tagged Tweet Streams[J]. ACM Transactions on Intelligent Systems, 2018, 9(3): 1-24.)
doi: 10.3390/ijgi9120733
[28] Francisco E, Fardos N, Bhatt A, et al. Impact of the COVID-19 Pandemic on Instagram and Influencer Marketing[J]. International Journal of Marketing Studies, 2021, 13(2): 20.
doi: 10.5539/ijms.v13n2p20
[28] (Zhao S L, Lyu M R, King I. STELLAR:Spatial-Temporal Latent Ranking Model for Successive POI Recommendation[A]// Point-of-Interest Recommendation in Location-Based Social Networks[M]. 2018: 79-94.)
doi: 10.5539/ijms.v13n2p20
[29] Son D, Shim K. Improving on Matrix Factorization for Recommendation Systems by Using a Character-Level Convolutional Neural Network[J]. KIISE Transactions on Computing Practices, 2018, 24(2): 93-98.
doi: 10.5626/KTCP.2018.24.2.93
[30] 徐新黎, 肖云月, 龙海霞, 等. 基于矩阵分解的属性网络嵌入和社区发现算法[J]. 计算机科学, 2021, 48(12): 204-211.
doi: 10.11896/jsjkx.210300060
[30] (Xu Xinli, Xiao Yunyue, Long Haixia, et al. Attributed Network Embedding Based on Matrix Factorization and Community Detection[J]. Computer Science, 2021, 48(12): 204-211.)
doi: 10.11896/jsjkx.210300060
[31] Roy D, Panda P, Roy K. Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning[J]. Neural Networks, 2020, 121: 148-160.
doi: S0893-6080(19)30271-0 pmid: 31563011
[32] Taheri M, Farnaghi M, Alimohammadi A, et al. Point-of-Interest Recommendation Using Extended Random Walk with Restart on Geographical-Temporal Hybrid Tripartite Graph[J]. Journal of Spatial Science, 2021: 1-19.
[33] Bhavana P, Padmanabhan V. Matrix Factorization of Large Scale Data Using Multistage Matrix Factorization[J]. Applied Intelligence, 2021, 51(6): 4016-4028.
doi: 10.1007/s10489-020-01957-0
[34] Yang F, Wang H Q, Fu J J. Improvement of Recommendation Algorithm Based on Collaborative Deep Learning and Its Parallelization on Spark[J]. Journal of Parallel and Distributed Computing, 2021, 148: 58-68.
doi: 10.1016/j.jpdc.2020.09.014
[35] Kim D, Park C, Oh J, et al. Deep Hybrid Recommender Systems via Exploiting Document Context and Statistics of Items[J]. Information Sciences, 2017, 417: 72-87.
doi: 10.1016/j.ins.2017.06.026
[36] Xu H B, Jiang C S. Research on Context-Aware Group Recommendation Based on Deep Learning[J]. Neural Computing and Applications, 2020, 32(6): 1745-1754.
doi: 10.1007/s00521-019-04286-7
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