Learning to model relatedness for news recommendation

Proceedings of the 20th International Conference on World Wide Web, WWW 2011

Volumn , Issue , 2011, Pages 57-66

  Lv, Yuanhua ^a   Moon, Taesup ^b   Kolari, Pranam ^b   Zheng, Zhaohui ^b   Wang, Xuanhui ^b   Chang, Yi ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b YAHOO RESEARCH   (United States)

Author keywords

Connection clarity; Learning; Novelty; Post click news recommendation; Relatedness; Relevance; Transition smoothness

Indexed keywords

CONNECTION CLARITY; LEARNING; NEWS RECOMMENDATION; NOVELTY; RELATEDNESS; RELEVANCE; TRANSITION SMOOTHNESS;

HARDWARE; WORLD WIDE WEB;

WEB SERVICES;

EID: 80055035612     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1963405.1963417     Document Type: Conference Paper

References (40)

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE, 17(6):734-749, 2005.
2. D. Agarwal, B.-C. Chen, and P. Elango. Explore/exploit schemes for web content optimization. In ICDM '09, pages 1-10, 2009.
3. J. Allan, C. Wade, and A. Bolivar. Retrieval and novelty detection at the sentence level. In SIGIR '03, pages 314-321, 2003.
4. D. Billsus and M. J. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2-3):147-180, 2000.
5. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, 2003.
6. T. Bogers and A. van den Bosch. Comparing and evaluating information retrieval algorithms for news recommendation. In RecSys '07, pages 141-144, 2007.
7. J. P. Callan. Passage-level evidence in document retrieval. In SIGIR '94, pages 302-310, Dublin, Ireland, 1994.
8. J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In SIGIR '98, pages 335-336, 1998.
9. S. Cronen-Townsend, Y. Zhou, and W. B. Croft. Predicting query performance. In SIGIR '02, pages 299-306, 2002.
10. A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW '07, pages 271-280, 2007.
11. H. Fang, T. Tao, and C. Zhai. A formal study of information retrieval heuristics. In SIGIR '04, pages 49-56, 2004.
12. J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 2000.
13. D. Harman. Overview of the third text retrieval conference (trec-3). In TREC, 1994.
14. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR '99, pages 50-57, 1999.
15. K. S. Jones and C. J. van Rijsbergen. Report on the need for and the provision of an 'ideal' information retrieval test collection. Technical Report (British Library Research and Development Report No. 5266), Computer Laboratory, University of Cambridge, 1975.
16. M. Kaszkiel and J. Zobel. Effective ranking with arbitrary passages. Journal of the American Society for Information Science and Technology, 52(4):344-364, 2001.
17. K. Lang. Newsweeder: Learning to filter netnews. In in Proceedings of the 12th International Machine Learning Conference, 1995.
18. V. Lavrenko and W. B. Croft. Relevance-based language models. In SIGIR '01, pages 120-127, 2001.
19. J. Lin. Divergence measures based on the shannon entropy. IEEE Trans. Infor. Theory, 37:145-151, 1991.
20. T.-Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225-331, 2009.
21. X. Liu and W. B. Croft. Passage retrieval based on language models. In CIKM '02, pages 375-382, McLean, Virginia, USA, 2002.
22. Y. Lv and C. Zhai. A comparative study of methods for estimating query language models with pseudo feedback. In Proceedings of CIKM '09, 2009.
23. C. Macdonald, I. Ounis, and I. Soboroff. Overview of trec-2009 blog track. In TREC '09, 2009.
24. J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In SIGIR '98, pages 275-281, 1998.
25. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In CSCW '94, pages 175-186, 1994.
26. S. E. Robertson. The probability ranking principle in ir. pages 281-286, 1997.
27. S. E. Robertson and K. S. Jones. Relevance weighting of search terms. Journal of the American Society of Information Science, 27(3):129-146, 1976.
28. S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at trec-3. In TREC '94, pages 109-126, 1994.
29. J. J. Rocchio. Relevance feedback in information retrieval. In In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313-323. Prentice-Hall Inc., 1971.
30. G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society of Information Science, 41(4):288-297, 1990.
31. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18(11):613-620, 1975.
32. D. Shahaf and C. Guestrin. Connecting the dots between news articles. In KDD '10, pages 623-632, 2010.
33. A. Singhal. Modern information retrieval: a brief overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 24:2001, 2001.
34. A. Singhal, C. Buckley, and M. Mitra. Pivoted document length normalization. In SIGIR '96, pages 21-29, 1996.
35. H. Toda and R. Kataoka. A clustering method for news articles retrieval system. In WWW '05, pages 988-989, 2005.
36. Y. Yang, N. Bansal, W. Dakka, P. Ipeirotis, N. Koudas, and D. Papadias. Query by document. In WSDM '09, pages 34-43, 2009.
37. C. Zhai, W. W. Cohen, and J. Lafferty. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In SIGIR '03, pages 10-17, 2003.
38. C. Zhai and J. D. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR '01, pages 334-342, 2001.
39. Y. Zhang, J. Callan, and T. Minka. Novelty and redundancy detection in adaptive filtering. In SIGIR '02, pages 81-88, 2002.
40. Z. Zheng, H. Zha, T. Zhang, O. Chapelle, K. Chen, and G. Sun. A general boosting method and its application to learning ranking functions for web search. In NIPS '07. 2007.