Modeling search engine effectiveness for federated search

SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2005, Pages 83-90

  Si, Luo ^a   Callan, Jamie ^a  

^a Carnegie Mellon University   (United States)

Author keywords

model search engine effectiveness

Indexed keywords

MODEL SEARCH; MULTIPLE SEARCH; RETRIEVAL EFFECTIVENESS; SEARCH TECHNIQUE; UTILITY MAXIMIZATIONS; VIRTUAL SEARCH;

ALGORITHMS; INFORMATION RETRIEVAL; RESEARCH;

SEARCH ENGINES;

EID: 84885648816     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1076034.1076051     Document Type: Conference Paper

References (20)

1. R. Baeza-Yates, and B, Ribeiro-Neto. (1999). Modern Information Retrieval. ACM Press / Addison Wesley.
2. C. Buckley, A. Singhal, M. Mitra, and G. Salton. (1995). New retrieval approaches using SMART. In Proceedings of 1995 Text REtrieval Conference (TREC-3). National Institute of Standards and Technology, special publication.
3. J. Callan. (2000). Distributed information retrieval. In W.B. Croft, editor, Advances in Information Retrieval. Kluwer Academic Publishers. (pp. 127-150).
4. J. Callan, W.B. Croft, and J. Broglio. (1995). TREC and TIPSTER experiments with INQUERY. Information Processing and Management, 31(3). (pp. 327-343).
5. T. T. Avrahami, L. Yau, L. Si, and J. Callan. The FedLemur project: Federated search in the real world. Journal of the American Society for Information Science and Technology. (In Press).
6. N. Craswell. (2000). Methods for distributed information retrieval. Ph. D. thesis, The Australian Nation University.
7. D. D'Souza, J. Thom, and J. Zobel. (2000). A comparison of techniques for selecting text collections. In Proceedings of the 11th Australasian Database Conference.
8. N. Fuhr. (1999). A Decision-Theoretic approach to database selection in networked IR. ACM Transactions on Information Systems, 17(3). (pp. 229-249).
9. L. Gravano, C. Chang, H. Garcia-Molina, and A. Paepcke. (1997). STARTS: Stanford proposal for internet metasearching. In Proceedings of the 20th ACM-SIGMOD International Conference on Management of Data.
10. L. Gravano, P. Ipeirotis and M. Sahami. (2003). QProber: A System for Automatic Classification of Hidden-Web Databases. ACM Transactions on Information Systems, 21(1).
11. The lemur toolkit. http://www.cs.cmu.edu/~lemur
12. J. Lu and J. Callan. (2003). Content-based information retrieval in peer-to-peer networks. In Proceedings of the 12th International Conference on Information and Knowledge Management.
13. K.L. Liu, W. Meng, C. Yu. (2001). Discovery of similarity computations of search engines, Proceedings of the ninth international conference on Information and knowledge management.
14. W. Meng, C.T. Yu and K.L. Liu. (2002). Building efficient and effective metasearch engines. ACM Comput. Surv. 34(1).
15. H. Nottelmann and N. Fuhr. (2003). Evaluating different method of estimating retrieval quality for resource selection. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
16. L. Si and J. Callan. (2003). Relevant document distribution estimation method for resource selection. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
17. L. Si and J. Callan. (2003). A Semi-Supervised learning method to merge search engine results. ACM Transactions on Information Systems, 21(4). (pp. 457-491).
18. L. Si and J. Callan. (2004). Unified Utility Maximization Framework for Resource Selection. In Proceedings of the ninth international conference on Information and knowledge management.
19. C.X. Zhai and J. Lafferty. (2001). A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
20. st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.