Introduction
In the web age, publishing information and opinions online is very easy and fast. Since the web is reachable by a huge number of grassroots people, the number and scale of social networking sites are growing at a tremendous speed. It is an interesting thing to find out information, news & events, opinions, etc., exchanged in these sites. Thus quite a few researchers focus on this and some related issues. One major characteristic of these social networking sites is their dynamic nature. When new things or themes appear, they are discussed in quirk, and then forgotten very quickly. It is also true that the thriving and decline of such sites may happen very quickly. How to cope with this dynamic environment is a challenging issue for the information/opinion search services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference, New Orleans, Louisiana, USA, pp. 276–284 (September 2001)
Bartell, B.T., Cottrell, G.W., Belew, R.K.: Automatic combination of multiple ranked retrieval systems. In: Proceedings of ACM SIGIR 1994, Dublin, Ireland, pp. 173–184 (July 1994)
Bigot, A., Chrisment, C., Dkaki, T., Hubert, G., Mothe, J.: Fusing different information retrieval systems according to query-topics: a study based on correlation in information retrieval systems and trec topics. Information. Retrieval 14(6), 617–648 (2011)
Calvé, A.L., Savoy, J.: Database merging strategy based on logistic regression. Information Processing & Management 36(3), 341–359 (2000)
Diamond, T., Liddy, E.D.: Dynamic data fusion. In: Proceedings of TIPSTER 1998 workshop, Baltimore, USA, pp. 123–128 (October 1998)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the Tenth International World Wide Web Conference, pp. 613–622, Hong Kong, China (May 2001)
Farah, M., Vanderpooten, D.: An outranking approach for rank aggregation in information retrieval. In: Proceedings of the 30th ACM SIGIR Conference, Amsterdam, The Netherlands, pp. 591–598 (July 2007)
Fox, E.A., Koushik, M.P., Shaw, J., Modlin, R., Rao, D.: Combining evidence from multiple searches. In: The First Text REtrieval Conference (TREC-1), Gaitherburg, MD, USA, March 1993, pp. 319–328 (March 1993)
Fox, E.A., Shaw, J.: Combination of multiple searches. In: The Second Text REtrieval Conference (TREC-2), Gaitherburg, MD, USA, pp. 243–252 (August 1994)
Lillis, D., Toolan, F., Collier, R., Dunnion, J.: Probfuse: a probabilistic approach to data fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference, Seattle, Washington, USA, pp. 139–146 (August 2006)
Montague, M., Aslam, J.A.: Condorcet fusion for improved retrieval. In: Proceedings of ACM CIKM Conference, McLean, VA, USA, pp. 538–548 (November 2002)
Renda, M.E., Straccia, U.: Web metasearch: rank vs. score based rank aggregation methods. In: Proceedings of ACM 2003 Symposium of Applied Computing, Melbourne, USA, pp. 841–846 (April 2003)
Vogt, C.C., Cottrell, G.W.: Predicting the performance of linearly combined IR systems. In: Proceedings of the 21st Annual ACM SIGIR Conference, Melbourne, Australia, pp. 190–196 (August 1998)
Vogt, C.C., Cottrell, G.W.: Fusion via a linear combination of scores. Information Retrieval 1(3), 151–173 (1999)
Wu, S.: Linear combination of component results in information retrieval. Data & Knowledge Engineering 71(1), 114–126 (2012)
Wu, S., Bi, Y., Zeng, X., Han, L.: Assigning appropriate weights for the linear combination data fusion method in information retrieval. Information Processing & Management 45(4), 413–426 (2009)
Wu, S., McClean, S.: Improving high accuracy retrieval by eliminating the uneven correlation effect in data fusion. Journal of American Society for Information Science and Technology 57(14), 1962–1973 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, S., Xing, Y., Li, J., Bi, Y. (2012). Adaptive Data Fusion Methods for Dynamic Search Environments. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-35341-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35340-6
Online ISBN: 978-3-642-35341-3
eBook Packages: Computer ScienceComputer Science (R0)