A collective Bayesian Poisson factorization model for cold-start local event recommendation

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2015-August, Issue , 2015, Pages 1455-1464

  Zhang, Wei ^a   Wang, Jianyong ^a,b  

^a TSINGHUA UNIVERSITY   (China)

^b JIANGSU NORMAL UNIVERSITY   (China)

Author keywords

Bayesian poisson factorization; Cold start recommendation; Event recommendation; Event based social networks

Indexed keywords

DATA MINING; FACTORIZATION; LIFE CYCLE; SOCIAL NETWORKING (ONLINE);

BAYESIAN; COLD-START RECOMMENDATIONS; COLLECTIVE MATRIX FACTORIZATIONS; ESSENTIAL CHARACTERISTIC; EVENT RECOMMENDATION; EVENT-BASED; FACTORIZATION MODEL; LOCATION INFORMATION;

BAYESIAN NETWORKS;

EID: 84954191049     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2783258.2783336     Document Type: Conference Paper

References (29)

1. N. Aizenberg, Y. Koren, and O. Somekh. Build your own music recommender by modeling internet radio streams. In WWW, Lyon, France, April 16-20, pages 1-10, 2012.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
3. K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, and Y. Yu. Collaborative personalized tweet recommendation. In SIGIR, Portland, OR, USA, August 12-16, pages 661-670, 2012.
4. R. Du, Z. Yu, T. Mei, Z. Wang, Z. Wang, and B. Guo. Predicting activity attendance in event-based social networks: content, context and social influence. In Ubicomp, Seattle, WA, USA, September 13-17, pages 425-434, 2014.
5. P. Gopalan, L. Charlin, and D. M. Blei. Content-based recommendations with poisson factorization. In NIPS, December 8-13, Montreal, Quebec, Canada, pages 3176-3184, 2014.
6. P. Gopalan, J. M. Hofman, and D. M. Blei. Scalable recommendation with poisson factorization. CoRR, vol. abs/1311.1704, 2013.
7. M. D. Hoffman, D. M. Blei, C. Wang, and J. W. Paisley. Stochastic variational inference. Journal of Machine Learning Research, 14(1):1303-1347, 2013.
8. L. Hu, A. Sun, and Y. Liu. Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In SIGIR, Gold Coast, QLD, Australia, July 06-11, pages 345-354, 2014.
9. N. L. Johnson, Z. Kotz, and A. W. Kemp. Univariate Discrete Distributions, 2nd Edition. Wiley & Sons, New York, 1993.
10. M. I. Jordan, Z. Ghahramani, T. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
11. A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In RecSys, Barcelona, Spain, September 26-30, pages 79-86, 2010.
12. H. Khrouf and R. Troncy. Hybrid event recommendation using linked data and user diversity. In RecSys, Hong Kong, China, October 12-16, pages 185-192, 2013.
13. D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In KDD, New York, NY, USA, August 24-27, pages 831-840, 2014.
14. G. Liao, Y. Zhao, S. Xie, and P. S. Yu. An effective latent networks fusion based model for event recommendation in offline ephemeral social networks. In CIKM, San Francisco, CA, USA, October 27-November 1, pages 1655-1660, 2013.
15. B. Liu, Y. Fu, Z. Yao, and H. Xiong. Learning geographical preferences for point-of-interest recommendation. In KDD, Chicago, IL, USA, August 11-14, pages 1043-1051, 2013.
16. X. Liu, Q. He, Y. Tian, W. Lee, J. McPherson, and J. Han. Event-based social networks: linking the online and offline social worlds. In KDD, Beijing, China, August 12-16, pages 1032-1040, 2012.
17. H. Ma, C. Liu, I. King, and M. R. Lyu. Probabilistic factor models for web site recommendation. In SIGIR, Beijing, China, July 25-29, pages 265-274, 2011.
18. E. Minkov, B. Charrow, J. Ledlie, S. J. Teller, and T. Jaakkola. Collaborative future event recommendation. In CIKM, Toronto, Ontario, Canada, October 26-30, pages 819-828, 2010.
19. M. J. Pazzani and D. Billsus. Content-based recommendation systems. In The Adaptive Web, Methods and Strategies of Web Personalization, pages 325-341, 2007.
20. T.-A. N. Pham, X. Li, G. Cong, and Z. Zhang. A general graph-based model for recommendation in event-based social networks. In ICDE, Seoul, Korea, April 13-17, 2015.
21. Z. Qiao, P. Zhang, Y. Cao, C. Zhou, L. Guo, and B. Fang. Combining heterogenous social and geographical information for event recommendation. In AAAI, July 27-31, Québec City, Québec, Canada, pages 145-151, 2014.
22. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, Vancouver, British Columbia, Canada, December 3-6, pages 1257-1264, 2007.
23. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML, Helsinki, Finland, June 5-9, pages 880-887, 2008.
24. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In SIGIR, August 11-15, Tampere, Finland, pages 253-260, 2002.
25. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In SIGIR, Las Vegas, Nevada, USA, August 24-27, pages 650-658, 2008.
26. C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In KDD, San Diego, CA, USA, August 21-24, pages 448-456, 2011.
27. M. Ye, P. Yin, W. Lee, and D. L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, Beijing, China, July 25-29, pages 325-334, 2011.
28. W. Zhang, J. Wang, and W. Feng. Combining latent factor model with location features for event-based group recommendation. In KDD, Chicago, IL, USA, August 11-14, pages 910-918, 2013.
29. V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. Collaborative location and activity recommendations with GPS history data. In WWW, Raleigh, North Carolina, USA, April 26-30, pages 1029-1038, 2010.