Abstract
In data fusion, the linear combination method is a very flexible method since different weights can be assigned to different systems. However, it remains an open question that which weighting schema is good. In many cases, a simple weighting schema was used: for a system, its weight is assigned as its average performance over a group of training queries. In this paper, we empirically investigate the weighting issue. We find that, a series of power functions of average performance, which can be implemented as efficiently as the simple weighting schema, is more effective than the simple weighting schema for data fusion. We also investigate combined weights which concern both performance of component results and dissimilarity among component results. Further performance improvement on data fusion is achievable by using the combined weights.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference, New Orleans, Louisiana, USA, September 2001, pp. 276–284 (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, July 1994, pp. 173–184 (1994)
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 (1993)
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, August 2006, pp. 139–146 (2006)
Montague, M., Aslam, J.A.: Condorcet fusion for improved retrieval. In: Proceedings of ACM CIKM Conference, McLean, VA, USA, November 2002, pp. 538–548 (2002)
Thompson, P.: Description of the PRC CEO algorithms for TREC. In: The First Text REtrieval Conference (TREC-1), Gaitherburg, MD, USA, March 1993, pp. 337–342 (1993)
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, August 1998, pp. 190–196 (1998)
Vogt, C.C., Cottrell, G.W.: Fusion via a linear combination of scores. Information Retrieval 1(3), 151–173 (1999)
Wu, S., Crestani, F.: Data fusion with estimated weights. In: Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management, McLean, VA, USA, November 2002, pp. 648–651 (2002)
Wu, S., McClean, S.: Data fusion with correlation weights. In: Proceedings of the 27th European Conference on Information Retrieval, Santiago de Composite, Spain, March 2005, pp. 275–286 (2005)
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
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, S., Bi, Y., Zeng, X., Han, L. (2008). The Experiments with the Linear Combination Data Fusion Method in Information Retrieval. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds) Progress in WWW Research and Development. APWeb 2008. Lecture Notes in Computer Science, vol 4976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78849-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-540-78849-2_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78848-5
Online ISBN: 978-3-540-78849-2
eBook Packages: Computer ScienceComputer Science (R0)