POI Recommendation Based on Geographic and Social Relationship Preferences
Yan Wen1(),Lijian Ma1,Qingtian Zeng2,Wenyan Guo1
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China 2College of Electronic Communications and Physics, Shandong University of Science and Technology, Qingdao 266590, China
[Objective] This study tries to improve the POI recommendation based on user’s geographic information and social relationships. [Methods] First, we proposed a MFDR model (MF with Distance-entropy and Refined-social-regularization), which introduced the concept of distance-entropy to refine user’s preferences and the frequency-based user-interest-matrix. Then, we applied the user-relationship-interest-matrix to refine the preferences with their social-relationship. Finally, we used the regularization-based matrix factorization method to factorize the user-preference-matrix and user-relationship-interest-matrix to ensure their consistency. [Results] We examined the new model with Gowalla and Brightkite check-in datasets, and found it outperformed existing POI recommendation algorithms. When the number of latent factors was 10 and the number of recommended POI was 10, the precision and recall of MFDR on Gowalla reached 4.47% and 9.95%. These results were 30.71% and 28.93% higher than those of traditional POI recommendation models. [Limitations] The expeimental datasets need to be expanded. [Conclusions] The proposed MFDR model based on geographical preference refinement and social-relationship preference implicit analysis is an effective way to recommend POI.
温彦,马立健,曾庆田,郭文艳. 基于地理信息偏好修正和社交关系偏好隐式分析的POI推荐 *[J]. 数据分析与知识发现, 2019, 3(8): 30-39.
Yan Wen,Lijian Ma,Qingtian Zeng,Wenyan Guo. POI Recommendation Based on Geographic and Social Relationship Preferences. Data Analysis and Knowledge Discovery, 2019, 3(8): 30-39.
( Meng Xiangwu, Liu Shudong, Zhang Yujie , et al. Research on Social Recommender Systems[J]. Journal of Software, 2015,26(6):1356-1372.)
[2]
Ma H, Zhou D, Liu C , et al. Recommender Systems with Social Regularization [C]// Proceedings of the 4th ACM International Conference on Web Search and Data Mining, Hong Kong, China. ACM, 2011: 287-296.
[3]
Ye M, Yin P, Lee W C , et al. Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation [C]// Proceedings of the 4th ACM International Conference on Web Search and Data Mining, Hong Kong, China. ACM, 2011: 325-334.
[4]
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 26th AAAI Conference on Artificial Intelligence, Toronto, Ontario, Canada. AAAI Press, 2012,12:17-23.
[5]
Liu F, Hong J L . Use of Social Network Information to Enhance Collaborative Filtering Performance[J]. Expert Systems with Applications, 2010,37(7):4772-4778.
[6]
Gao H, Tang J, Hu X , et al. Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks [C]// Proceedings of the 7th ACM International Conference on Recommender Systems, Hong Kong, China. ACM, 2013: 93-100.
[7]
Rendle S . Social Network and Click-Through Prediction with Factorization Machines [C]// Proceedings of the ACM KDD Cup Workshop, New York, NY, USA. ACM, 2012.
[8]
Ference G, Ye M, Lee W C . Location Recommendation for Out-of-Town Users in Location-Based Social Networks [C]// Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, CA, USA. ACM, 2013: 721-726.
[9]
Griesner J B, Abdessalem T, Naacke H . POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences [C]// Proceedings of the 9th ACM International Conference on Recommender Systems, Vienna, Austria. ACM, 2015: 301-304.
( Yang Fengrui, Zheng Yunjun, Zhang Chang . Hybrid Recommendation Algorithm Based on Probability Matrix Factorization[J]. Journal of Computer Applications, 2017,38(3):644-649.)
[11]
Liu X, Song Y, Aggarwal C , et al. BiCycle: Item Recommendation with Life Cycles [C]// Proceedings of the 17th IEEE International Conference on Data Mining, New Orleans, LA, USA. IEEE, 2017: 297-306.
[12]
Lian D, Zhao C, Xie X , et al. GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation [C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA. ACM, 2014: 831-840.
[13]
Zhao S, King I, Lyu M R . Capturing Geographical Influence in POI Recommendations [C]// Proceedings of the 20th International Conference on Neural Information Processing. 2013: 530-537.
[14]
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, Orlando, FL, USA. ACM, 2013: 334-343.
[15]
Ye M, Yin P, Lee W C . Location Recommendation for Location-Based Social Networks [C]// Proceedings of the 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, San Jose, CA, USA. ACM, 2010: 458-461.
[16]
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, Santiago, Chile. ACM, 2015: 443-452.
[17]
Li H, Ge Y, Hong R , et al. Point-of-Interest Recommendations: Learning Potential Check-ins from Friends [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA. ACM, 2016: 975-984.
[18]
Yu F, Che N, Li Z , et al. Friend Recommendation Considering Preference Coverage in Location-Based Social Networks [C]// Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, South Korea. Springer, 2017: 91-105.
[19]
Cover T M, Thomas J A . 信息论基础[M]. 阮吉寿, 张华译. 第2版. 北京: 机械工业出版社, 2008.
[19]
( Cover T M, Thomas J A. Elements of Information Theory[M]. Translated by Ruan Jishou, Zhang Hua. The 2nd Edition. Beijing: China Machine Press, 2008.)
[20]
Lee D D, Seung H S , et al. Learning the Parts of Objects by Non-Negative Matrix Factorization[J]. Nature, 1999,401(6755):788-791.
