Textual-geographical-social aware point-of-interest recommendation

doi:10.1016/S1005-8885(16)60066-3

中国邮电高校学报(英文版) ›› 2016, Vol. 23 ›› Issue (6): 24-33.doi: 10.1016/S1005-8885(16)60066-3

• Artificial Intelligence • 上一篇下一篇

Textual-geographical-social aware point-of-interest recommendation

任星怡

北京邮电大学

收稿日期:2016-06-16 修回日期:2016-09-22 出版日期:2016-12-31 发布日期:2016-12-30
通讯作者: 任星怡 E-mail:xyren@bupt.edu.cn

Textual-geographical-social aware point-of-interest recommendation

Xing-Yi REN

Received:2016-06-16 Revised:2016-09-22 Online:2016-12-31 Published:2016-12-30
Contact: Xing-Yi REN E-mail:xyren@bupt.edu.cn

摘要/Abstract

摘要： The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through point-of-interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model (PMF) to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

关键词: location-based social networks, POI recommendation, topic model, geographical correlations, social correlations

Abstract: The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through point-of-interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model (PMF) to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

Key words: location-based social networks, POI recommendation, topic model, geographical correlations, social correlations

Xing-Yi REN,Song Meina, E Haihong, Song Junde. Textual-geographical-social aware point-of-interest recommendation[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2016, 23(6): 24-33.

参考文献

1. Farrahi K, Gatica-Perez D. Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology, 2011, 2(1): Article 3

2. Ye M, Shou D, Lee W C, et al. On the semantic annotation of places in location-based social networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11), Aug 21-24, 2011, San Diego, CA, USA. New York, NY, USA: ACM, 2011: 520-528

3. Yin Z J, Cao L L, Han J W, et al. Geographical topic discovery and comparison. Proceedings of the 20th International Conference on World Wide Web (WWW’11), Mar 28-Apr 1, 2011, Hyderabad, India. New York, NY, USA: ACM, 2011: 247-256

4. Ferrari L, Rosi A, Mamei M, et al. Extracting urban patterns from location-based social networks. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Nov 1-4, 2011, Chicago, IL, USA. New York, NY, USA: ACM, 2011: 9-16

5. Agarwal D, Chen B C. Flda: matrix factorization through latent dirichlet allocation. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM’10), Feb 4-6, 2010, New York, NY, USA. New York, NY, USA: ACM, 2010: 91-100

6. Pennacchiotti M, Gurumurthy S. Investigating topic models for social media user recommendation. Proceedings of the 20th International Conference on World Wide Web (WWW’11), Mar 28-Apr 1, 2011, Hyderabad, India. New York, NY, USA: ACM, 2011: 101-102

7. Wang H, Terrovitis M, Mamoulis N. Location recommendation in location-based social networks using user check-in data. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 5-8, 2013, Orlando, FL, USA. New York, NY, USA: ACM, 2013: 374-383

8. Bao J, Zheng Y, Mokbel M F. Location-based and preference-aware recommendation using sparse geo-social networking data. Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 6-9, 2012, Redondo Beach, CA, USA. New York, NY, USA: ACM, 2012: 199-208

9. Liu B, Xiong H. Point-of-interest recommendation in location based social networks with topic and location awareness. Proceedings of the SIAM International Conference on Data Mining (SDM’13), May 2-4, 2013, Austin, TX, USA. New York, NY, USA: ACM, 2013: 396-404

10. Lian D F, Zhao C, Xie X, et al. Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14), Aug 24-27, 2014, New York, NY, USA. New York, NY, USA: ACM, 2014: 831-840

11. Hu L K, Sun A X, Liu Y. Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 6-11, 2014, Gold Coast, Australia. New York, NY, USA: ACM, 2014: 345-354

12. Ye M, Yin P F, Lee W C, et al. Exploiting geographical influence for collaborative point-of-interest recommendation. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 24-28, 2011, Beijing, China. New York, NY, USA: ACM, 2011: 325-334

13. Liu B, Fu Y J, Yao Z J, et al. Learning geographical preferences for point-of-interest recommendation. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13), Aug 11-14, 2013, Chicago, IL, USA. New York, NY, USA: ACM, 2013: 1043-1051

14. Yuan Q, Cong G, Ma Z Y, et al. Time-aware point-of-interest recommendation. Proceedings of the 36th ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 28-Aug 1, 2013, Dublin, Ireland. New York, NY, USA: ACM, 2013: 363-372

15. Zhang J D, Chow C Y. iGSLR: personalized geo-social location recommendation–a kernel density estimation approach. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 5-8, 2013, Orlando, FL, USA. New York, NY, USA: ACM, 2013: 334-343

16. Cheng C, Yang H Q, King I, et al. Fused matrix factorization with geographical and social influence in location-based social networks. Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI’12), Jul 22-26, 2012, Toronto, Canada. Menlo Park, CA , USA: AAAI, 2012: 17-23

17. Zhang J D, Chow C Y. CoRe: exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations. Journal of Information Sciences, 2015, 293(1): 163-181

18. Zhang J D, Chow C Y, Li Y H. Lore: exploiting sequential influence for location recommendations. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 4-7, 2014, Dallas, TX, USA. New York, NY, USA: ACM, 2014: 103-112

19. Ying J J C, Kuo W N, Tseng V S, et al. Mining user check-in behavior with a random walk for urban point-of-interest recommendations. Journal of the ACM Transactions on Intelligent Systems and Technology, 2014, 5(3): Article 40/1-26

20. Liu X, Wu W. Learning context-aware latent representations for context-aware collaborative filtering. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug 9-13, 2015, Santiago, Chile. New York, NY, USA: ACM, 2015: 887-890

21. Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993-1022

22. Griffiths T L, Steyvers M. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(Sup 1): 5228-5235

23. Cheng Z Y, Caverlee J, Lee K, et al. Exploring millions of footprints in location sharing services. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM’11), Jul 17-21, 2011, Barcelona, Spain. Menlo Park, CA , USA: AAAI, 2011: 81-88

24. Yin H Z, Sun Y Z, Cui B, et al. Lcars: a location-content-aware recommender system. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13), Aug 11-14, 2013, Chicago, IL, USA. New York, NY, USA: ACM, 2013: 221-229

Textual-geographical-social aware point-of-interest recommendation

Textual-geographical-social aware point-of-interest recommendation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价