Learning to rank social update streams

SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2012, Pages 651-660

  Hong, Liangjie ^a   Bekkerman, Ron ^b   Adler, Joseph ^b   Davison, Brian D ^a  

^a Lehigh University   (United States)

^b LINKEDIN CORPORATION   (United States)

Author keywords

collaborative filtering; learning to rank; social network; social stream

Indexed keywords

COLLABORATIVE FILTERING; CONTENT FLOW; INFORMATION STREAMS; LATENT FACTOR; LEARNING TO RANK; NON-TRIVIAL; ON-THE-FLY; SOCIAL MEDIA; SOCIAL NETWORKS; SOCIAL STREAM; VIABLE SOLUTIONS;

INFORMATION RETRIEVAL;

SOCIAL NETWORKING (ONLINE);

EID: 84866633842     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2348283.2348371     Document Type: Conference Paper

References (34)

1. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In KDD 2009, pages 19-28.
2. F. Benevenuto, T. Rodrigues, M. Cha, and V. Almeida. Characterizing user behavior in online social networks. In SIGCOMM 2009, pages 49-62.
3. C. Borgs, J. T. Chayes, B. Karrer, B. Meeder, R. Ravi, R. Reagans, and A. Sayedi. Game-theoretic models of information overload in social networks. In Workshop on Algorithms and Models for the WebGraph 2010, pages 146-161.
4. S. Budalakoti and R. Bekkerman. Bimodal invitation-navigation fair bets model for authority identification in a social network. In WWW 2012, pages 709-718.
5. B. Cao, D. Shen, K. Wang, and Q. Yang. Clickthrough log analysis by collaborative ranking. In AAAI 2010.
6. O. Chapelle and M. Wu. Gradient descent optimization of smoothed information retrieval metrics. Information Retrieval, 13:216-235, 2010.
7. J. Chen, R. Nairn, and E. Chi. Speak little and well: recommending conversations in online social streams. In CHI 2011, pages 217-226.
8. J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi. Short and tweet: experiments on recommending content from information streams. In CHI 2010, pages 1185-1194.
9. N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In WSDM 2008, pages 87-94.
10. M. De Choudhury, S. Counts, and M. Czerwinski. Identifying relevant social media content: leveraging information diversity and user cognition. In Hypertext and Hypermedia 2011, pages 161-170.
11. Y. Duan, L. Jiang, T. Qin, M. Zhou, and H.-Y. Shum. An empirical study on learning to rank of tweets. In COLING 2010, pages 295-303.
12. L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In WWW 2011, pages 57-58.
13. B. Hu, Y. Zhang, W. Chen, G. Wang, and Q. Yang. Characterizing search intent diversity into click models. In WWW 2011, pages 17-26.
14. T. Joachims. Optimizing search engines using clickthrough data. In KDD 2002, pages 133-142.
15. N. Koenigstein, G. Dror, and Y. Koren. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In RecSys 2011, pages 165-172.
16. T. G. Kolda and B. W. Bader. Tensor decompositions and applications. SIAM Review, 51:455-500, August 2009.
17. Y. Koren. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM TKDD, 4:1:1-1:24, January 2010.
18. Y. Koren and J. Sill. OrdRec: an ordinal model for predicting personalized item rating distributions. In RecSys 2011, pages 117-124.
19. T.-Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225-331, 2009.
20. S. Muralidharan, L. Rasmussen, D. Patterson, and J.-H. Shin. Hope for haiti: An analysis of facebook and twitter usage during the earthquake relief efforts. Public Relations Review, 37(2):175-177, 2011.
21. M. Naaman, J. Boase, and C.-H. Lai. Is it really about me?: message content in social awareness streams. In CSCW 2010, pages 189-192.
22. T. Qin, T.-Y. Liu, and H. Li. A general approximation framework for direct optimization of information retrieval measures. Information Retrieval, 13:375-397, 2010.
23. S. Rendle. Factorization machines with libFM. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3):57:1-57:22, 2012.
24. S. Rendle, L. Balby Marinho, A. Nanopoulos, and L. Schmidt-Thieme. Learning optimal ranking with tensor factorization for tag recommendation. In KDD 2009, pages 727-736.
25. S. Rendle, C. Freudenthaler, Z. Gantner, and S.-T. Lars. BPR: Bayesian personalized ranking from implicit feedback. In UAI 2009, pages 452-461.
26. S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM 2010, pages 81-90.
27. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML 2008, pages 880-887.
28. A. P. Singh and G. J. Gordon. A unified view of matrix factorization models. In ECML 2008, pages 358-373.
29. L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In SDM 2010, pages 211-222.
30. S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: joint friendship and interest propagation in social networks. In WWW 2011, pages 537-546.
31. S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. In SIGIR 2011, pages 295-304.
32. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR 2007, pages 271-278.
33. Y. Zhang, W. Chen, D. Wang, and Q. Yang. User-click modeling for understanding and predicting search-behavior. In KDD 2011, pages 1388-1396.
34. M. Zinkevich, A. Smola, M. Weimer, , and L. Li:. Parallelized stochastic gradient descent. In NIPS 2010, pages 1-9.