An optimization framework for merging multiple result lists

International Conference on Information and Knowledge Management, Proceedings

Volumn 19-23-Oct-2015, Issue , 2015, Pages 303-312

  Lee, Chia Jung ^a   Ai, Qingyao ^a   Croft, W Bruce ^a   Sheldon, Daniel ^a  

^a The Manning College of Information and Computer Sciences   (United States)

Author keywords

Collection fusion; Data fusion; Deep neural network; Learning to merge

Indexed keywords

DATA ACQUISITION; DATA FUSION; KNOWLEDGE MANAGEMENT; WORLD WIDE WEB;

COLLECTION FUSION; DATA FUSION TECHNIQUE; DEEP NEURAL NETWORKS; DOCUMENT COLLECTION; FEDERATED WEB SEARCH; LEARNING-BASED APPROACH; NETWORK-BASED APPROACH; OPTIMIZATION FRAMEWORK;

MERGING;

EID: 84958246370     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2806416.2806489     Document Type: Conference Paper

References (42)

1. J. A. Aslam and M. Montague. Bayes optimal metasearch: A probabilistic model for combining the results of multiple retrieval systems (poster session). In Proceedings of SIGIR, SIGIR '00, pages 379-381, 2000.
2. J. A. Aslam and M. Montague. Models for metasearch. In Proceedings of SIGIR, SIGIR '01, pages 276-284, 2001.
3. S. M. Beitzel, E. C. Jensen, A. Chowdhury, D. Grossman, O. Frieder, and N. Goharian. On fusion of effective retrieval strategies in the same information retrieval system. JASIST, 55:859-868, 2004.
4. Y. Bengio. Learning deep architectures for ai. Found. Trends Mach. Learn., 2(1):1-127, Jan. 2009.
5. Y. Bengio. Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade, pages 437-478. Springer, 2012.
6. C. Burges, R. Ragno, and Q. Le. Learning to rank with non-smooth cost functions. In Advances in Neural Information Processing Systems 19, January 2007.
7. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of ICML, ICML '05, pages 89-96, 2005.
8. C. J. Burges. From ranknet to lambdarank to lambdamart: An overview. Technical report, June 2010.
9. J. Callan. Distributed information retrieval. In In: Advances in Information Retrieval, pages 127-150. Kluwer Academic Publishers, 2000.
10. J. P. Callan, Z. Lu, and W. B. Croft. Searching distributed collections with inference networks. In Proceedings of SIGIR, SIGIR '95, pages 21-28, 1995.
11. G. V. Cormack, C. L. A. Clarke, and S. Buettcher. Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of SIGIR, SIGIR '09, pages 758-759, 2009.
12. W. B. Croft. Combining approaches to information retrieval. Advances in Information Retrieval, Chapter 1, The Information Retrieval Series, 7:1-36, 2000.
13. T. Demeester, D. Trieschnigg, D. Nguyen, and D. Hiemstra. Overview of the trec 2013 federated web search track. In Proceedings of TREC, 2013.
14. T. Demeester, D. Trieschnigg, D. Nguyen, Z. K., and D. Hiemstra. Overview of the trec 2014 federated web search track. In Proceedings of TREC, 2014.
15. L. Deng, X. He, and J. Gao. Deep stacking networks for information retrieval. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 3153-3157, May 2013.
16. L. Deng and D. Yu. Deep learning: Methods and applications. Technical Report MSR-TR-2014-21, January 2014.
17. E. A. Fox and J. A. Shaw. Combination of multiple searches. In The Second Text REtrieval Conference (TREC-2), pages 243-252, 1994.
18. F. Guan, S. Zhang, C. Liu, X. Yu, Y. Liu, and X. Cheng. Ictnet at federated web search track 2014. In Proceedings of TREC, 2014.
19. D. Hawking and P. Thomas. Server selection methods in hybrid portal search. In Proceedings of SIGIR, SIGIR '05, pages 75-82, 2005.
20. P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of CIKM, CIKM '13, pages 2333-2338, 2013.
21. S. Jin and M. Lan. Simple may be best - a simple and effective method for federated web search via search engine impact factor estimation. In Proceedings of TREC, 2014.
22. A. Khudyak Kozorovitsky and O. Kurland. Cluster-based fusion of retrieved lists. In Proceedings of SIGIR, SIGIR '11, pages 893-902, 2011.
23. O. Kurland, A. Shtok, D. Carmel, and S. Hummel. A unified framework for post-retrieval query-performance prediction. In Proceedings of ICTIR, ICTIR'11, pages 15-26, 2011.
24. J. H. Lee. Analyses of multiple evidence combination. In Proceedings of SIGIR, SIGIR '97, pages 267-276, 1997.
25. D. Lillis, F. Toolan, R. Collier, and J. Dunnion. Probabilistic data fusion on a large document collection. Artif. Intell. Rev., 26(1-2):23-34, Oct. 2006.
26. D. Lillis, F. Toolan, R. Collier, and J. Dunnion. Extending probabilistic data fusion using sliding windows. In Proceedings of ECIR, ECIR'08, pages 358-369, 2008.
27. G. Mesnil, X. He, L. Deng, and Y. Bengio. Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding. In INTERSPEECH, pages 3771-3775, 2013.
28. D. Metzler and W. B. Croft. A markov random field model for term dependencies. In Proceedings of SIGIR, SIGIR '05, pages 472-479, 2005.
29. T. Mikolov, W.-t. Yih, and G. Zweig. Linguistic regularities in continuous space word representations. In HLT-NAACL, pages 746-751. Citeseer, 2013.
30. M. Montague and J. A. Aslam. Condorcet fusion for improved retrieval. In Proceedings of CIKM, CIKM '02, pages 538-548, 2002.
31. A. Mourao, F. Martins, and J. Magalhaes. Novasearch at trec 2013 federated web search track: Experiments with rank fusion. In The 22nd Text Retrieval Conference, TREC 13, 2013.
32. D. Sheldon, M. Shokouhi, M. Szummer, and N. Craswell. Lambdamerge: Merging the results of query reformulations. In Proceedings of WSDM, WSDM '11, pages 795-804, 2011.
33. Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil. A latent semantic model with convolutional-pooling structure for information retrieval. In Proceedings of CIKM, CIKM '14, pages 101-110, 2014.
34. M. Shokouhi. Segmentation of search engine results for effective data-fusion. In Proceedings of ECIR, ECIR'07, pages 185-197, 2007.
35. M. Shokouhi and L. Si. Federated search. Found. Trends Inf. Retr., 5(1):1-102, Jan. 2011.
36. M. Shokouhi and J. Zobel. Robust result merging using sample-based score estimates. ACM Trans. Inf. Syst., 27(3):14:1-14:29, May 2009.
37. A. Shtok, O. Kurland, D. Carmel, F. Raiber, and G. Markovits. Predicting query performance by query-drift estimation. ACM Trans. Inf. Syst., 30(2):11:1-11:35, May 2012.
38. L. Si and J. Callan. A semisupervised learning method to merge search engine results. ACM Trans. Inf. Syst., 21(4):457-491, Oct. 2003.
39. C. C. Vogt and G. W. Cottrell. Fusion via a linear combination of scores. Inf. Retr., 1(3):151-173, Oct. 1999.
40. E. M. Voorhees, N. K. Gupta, and B. Johnson-laird. The collection fusion problem. In Proceedings of the TREC, pages 95-104, 1995.
41. Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Inf. Retr., 13(3):254-270, June 2010.
42. S. Wu and S. McClean. Performance prediction of data fusion for information retrieval. Inf. Process. Manage., 42(4):899-915, July 2006.