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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (5): 77-88    DOI: 10.11925/infotech.2096-3467.2021.1047
Current Issue | Archive | Adv Search |
Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors
Guo Lei,Liu Wenju,Wang Ze(),Ren Yueqiang
College of Computer Science and Technology, Tiangong University, Tianjin 300387, China
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[Objective] This paper tries to improve the recommendation algorithm for Location-Based Social Networks (LBSN) and reduce the impacts of sparse data on recommendation precision. [Methods] First, we used the adaptive spectral clustering technique to group the users. Then, we created the recommending candidates for the point of interests (POIs) visited by the users. Finally, we calculated the attracting scores of the candidate sets and generated the recommended POIs with higher scores. [Results] We examined the new model with two real LBSN data sets: Gowalla and Foursquare, and set the recommended number of POIs as 2. Our model’s precision reached 11.4% and 7.4%, which were 3.2% and 1.1% higher than the Lore model. The new model’s running time reduced to 50 644.53 s and 406 224.7 s (16 961.49 s and 227 248.6 s shorter than the benchmark model). [Limitations] The clustering algorithm could influence the screening of POIs. [Conclusions] The proposed model could effectively improve the recommendation precision of heterogeneous networks (i.e.,LBSN).

Key wordsSpectral Clustering      Point of Interests Recommendation      Location Based Social Network     
Received: 16 September 2021      Published: 21 June 2022
ZTFLH:  TP391  
Fund:Natural Science Foundation of Tianjin(19JCYBJC15800);Key Project Foundation of Tianjin(15ZXHLGX003901);National Natural Science Foundation of China(61702366)
Corresponding Authors: Wang Ze,ORCID: 0000-0001-6971-2004     E-mail:

Cite this article:

Guo Lei, Liu Wenju, Wang Ze, Ren Yueqiang. Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors. Data Analysis and Knowledge Discovery, 2022, 6(5): 77-88.

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LBSN Network
User Check-in Sequential Pattern
比较项 Foursqure Gowalla
签到记录数 4 648 106 2 688 134
用户数 18 828 7 272
兴趣点数 167 310 62 764
好友关系数 59 254 80 838
兴趣点种类数 229 177
Basic Information of the Datasets
Precision and Recall of POI Recommendation on Gowalla
Precision and Recall of POI Recommendation on Foursquare
F1 of POI Recommendation
Three Algorithms Operation Time
Influences of Different Factors on Precision
Influences of Different Factors on Recall
[1] Li X, Jiang M M, Hong H T, et al. A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization[J]. ACM Transactions on Information Systems, 2017, 35(4): Article No.31.
[2] Liu Y, Yang H, Sun G X, et al. Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network[J]. Ingénierie Des Systèmes d Information, 2020, 25(3): 359-364.
doi: 10.18280/isi.250310
[3] Cai W J, Wang Y F, Lv R H, et al. An Efficient Location Recommendation Scheme Based on Clustering and Data Fusion[J]. Computers & Electrical Engineering, 2019, 77: 289-299.
[4] Hu S H, Tu Z Y, Wang Z J, et al. A POI-Sensitive Knowledge Graph Based Service Recommendation Method[C]// Proceedings of the 2019 IEEE International Conference on Services Computing. IEEE, 2019: 197-201.
[5] Cheng C, Yang H, King I, et al. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks[C]// Proceedings of the 2012 AAAI Conference on A.pngicial Intelligence. 2012, 26(1): 17-23.
[6] Zhang J D, Chow C Y. iGSLR: Personalized Geo-Social Location Recommendation: A Kernel Density Estimation Approach[C]// Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2013: 334-343.
[7] Pan Z G, Cui L, Wu X Y, et al. Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation[J]. IEEE Access, 2019, 7: 99496-99507.
doi: 10.1109/ACCESS.2019.2930311
[8] 许朝, 孟凡荣, 袁冠, 等. 融合地点影响力的兴趣点推荐算法[J]. 计算机应用, 2019, 39(11): 3178-3183.
[8] ( Xu Chao, Meng Fanrong, Yuan Guan, et al. Point-of-Interest Recommendation Algorithm Combining Location Influence[J]. Journal of Computer Applications, 2019, 39(11): 3178-3183.)
[9] Ozsoy M G, Polat F, Alhajj R. Time Preference Aware Dynamic Recommendation Enhanced with Location, Social Network and Temporal Information[C]// Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2016: 909-916.
[10] Ding R F, Chen Z Z, Li X L. Spatial-Temporal Distance Metric Embedding for Time-Specific POI Recommendation[J]. IEEE Access, 2018, 6: 67035-67045.
doi: 10.1109/ACCESS.2018.2869994
[11] Rosário D L, Machado K, et al. TEMMUS: A Mobility Predictor Based on Temporal Markov Model with User Similarity[C]// Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. 2019: 594-607.
[12] Zhang J D, Chow C Y. GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015: 443-452.
[13] Zhang J D, Chow C Y, Li Y H. LORE: Exploiting Sequential Influence for Location Recommendations[C]// Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2014: 103-112.
[14] Li X, Wang Z J, Hu R L, et al. Recommendation Algorithm Based on Improved Spectral Clustering and Transfer Learning[J]. Pattern Analysis and Applications, 2019, 22(2): 633-647.
doi: 10.1007/s10044-017-0671-2
[15] 刘真, 王娜娜, 王晓东, 等. 位置社交网络中谱嵌入增强的兴趣点推荐算法[J]. 通信学报, 2020, 41(3): 197-206.
[15] ( Liu Zhen, Wang Nana, Wang Xiaodong, et al. Spectral Clustering and Embedding-Enhanced POI Recommendation in Location-Based Social Network[J]. Journal on Communications, 2020, 41(3): 197-206.)
[16] Luxburg U. A Tutorial on Spectral Clustering[J]. Statistics and Computing, 2007, 17(4): 395-416.
doi: 10.1007/s11222-007-9033-z
[17] 孔万增, 孙志海, 杨灿, 等. 基于本征间隙与正交特征向量的自动谱聚类[J]. 电子学报, 2010, 38(8): 1880-1885.
[17] ( Kong Wanzeng, Sun Zhihai, Yang Can, et al. Automatic Spectral Clustering Based on Eigengap and Orthogonal Eigenvector[J]. Acta Electronica Sinica, 2010, 38(8): 1880-1885.)
[18] Jaccard P. The Distribution of the Flora in the Alpine Zone[J]. New Phytologist, 1912, 11(2): 37-50.
doi: 10.1111/j.1469-8137.1912.tb05611.x
[19] Kim D, Seo D, Cho S, et al. Multi-Co-Training for Document Classification Using Various Document Representations: TF-IDF, LDA, and Doc2Vec[J]. Information Sciences, 2019, 477: 15-29.
doi: 10.1016/j.ins.2018.10.006
[20] Yuan H L, Tang Y C, Sun W J, et al. A Detection Method for Android Application Security Based on TF-IDF and Machine Learning[J]. PLoS One, 2020, 15(9): e0238694.
doi: 10.1371/journal.pone.0238694
[21] Feng W L, Zhu Q Y, Zhuang J, et al. An Expert Recommendation Algorithm Based on Pearson Correlation Coefficient and FP-Growth[J]. Cluster Computing, 2019, 22(3): 7401-7412.
doi: 10.1007/s10586-017-1576-y
[22] Shi J B, Malik J. Normalized Cuts and Image Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
doi: 10.1109/34.868688
[23] Hofmeyr D P. Improving Spectral Clustering Using the Asymptotic Value of the Normalized Cut[J]. Journal of Computational and Graphical Statistics, 2019, 28(4): 980-992.
doi: 10.1080/10618600.2019.1593180
[24] Silverman B W. Density Estimation for Statistics and Data Analysis[M]. New York: Routledge, 2018.
[25] Ogundele T J, Chow C Y, Zhang J D. SoCaST: Exploiting Social, Categorical and Spatio-Temporal Preferences for Personalized Event Recommendations[C]// Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 3rd International Symposium of Creative Computing. IEEE, 2017: 38-45.
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