Relevance feedback exploiting query-specific document manifolds

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 1957-1960

  Wang, Chang ^a   Yilmaz, Emine ^b,c   Szummer, Martin ^c  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b KOÇ UNIVERSITY   (Turkey)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

manifold learning; relevance feedback

Indexed keywords

DOCUMENT SIMILARITY; FEEDBACK DOCUMENTS; LEARNING TO RANK; MANIFOLD LEARNING; OVERFITTING; RELEVANCE FEEDBACK; RETRIEVED DOCUMENTS; SCORING FUNCTIONS; TERM WEIGHT;

KNOWLEDGE MANAGEMENT;

FEEDBACK;

EID: 83055186801     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063864     Document Type: Conference Paper

References (12)

1. J. A. Aslam, E. Kanoulas, V. Pavlu, S. Savev, and E. Yilmaz. Document selection methodologies for efficient and effective learning-to-rank. In SIGIR, 2009.
2. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. 2006.
3. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, pages 89-96, New York, NY, USA, 2005. ACM.
4. C. J. C. Burges, R. Ragno, and Q. V. Le. Learning to rank with nonsmooth cost functions. In NIPS, pages 193-200, 2006.
5. F. Chung. Spectral graph theory. 1997.
6. F. D. Diaz. Improving relevance feedback in language modeling with score regularization. In SIGIR, 2008.
7. J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In Proceedings of the 24th ACM SIGIR conference on Research and development in information retrieval, pages 111-119, New York, NY, USA, 2001. ACM.
8. V. Lavrenko and W. B. Croft. Relevance based language models. In Proceedings of the 24th ACM SIGIR conference on Research and development in information retrieval, pages 120-127, New York, NY, USA, 2001. ACM.
9. T. Qin, T. Liu, X. Zhang, D. Wang, and H. Li. Global ranking using continuous conditional random fields. In NIPS, 2008.
10. S. E. Robertson and K. Sparck Jones. Relevance weighting of search terms. pages 143-160, 1988.
11. J. Rocchio. Relevance Feedback in Information Retrieval, pages 313-323. 1971.
12. Z. Xu and R. Akella. A bayesian logistic regression model for active relevance feedback. In SIGIR, 2008.