Abstract
In data fusion, score normalization is a step to make scores, which are obtained from different component systems for all documents, comparable to each other. It is an indispensable step for effective data fusion algorithms such as CombSum and CombMNZ to combine them. In this paper, we evaluate four linear score normalization methods, namely the fitting method, Zero-one, Sum, and ZMUV, through extensive experiments. The experimental results show that the fitting method and Zero-one appear to be the two leading methods.
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
Amitay, E., Carmel, D., Lempel, R., Soffer, A.: Scaling ir-system evaluation using term relevance sets. In: Proceedings of the 27th Annual International ACM SIGIR Conference, Sheffield, UK, July 2004, pp. 10–17 (2004)
Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference, Gaithersburg, Maryland, USA, November 2003, pp. 24–37 (2003)
Fox, E.A., Shaw, J.: Combination of multiple searches. In: The Second Text REtrieval Conference (TREC-2), Gaitherburg, MD, USA, August 1994, pp. 243–252 (1994)
Lee, J.H.: Analysis of multiple evidence combination. In: Proceedings of the 20th Annual International ACM SIGIR Conference, Philadelphia, Pennsylvania, USA, July 1997, pp. 267–275 (1997)
Montague, M., Aslam, J.A.: Relevance score normalization for metasearch. In: Proceedings of ACM CIKM Conference, Berkeley, USA, November 2001, pp. 427–433 (2001)
Soboroff, I., Nicholas, C., Cahan, P.: Ranking retrieval systems without relevance judgments. In: Proceedings of 24th Annual International ACM SIGIR Conference, New Orleans, Louisiana, USA, September 2001, pp. 66–73 (2001)
TREC., http://trec.nist.gov/
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., Crestani, F.: Methods for ranking information retrieval systems without relevance judgments. In: Proceedings of the 2003 ACM Symposium on applied computing (SAC), Melbourne, Florida, USA, March 2003, pp. 811–816 (2003)
Wu, S., McClean, S.: Performance prediction of data fusion for information retrieval. Information Processing & Management 42(4), 899–915 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, S., Crestani, F., Bi, Y. (2006). Evaluating Score Normalization Methods in Data Fusion. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_57
Download citation
DOI: https://doi.org/10.1007/11880592_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45780-0
Online ISBN: 978-3-540-46237-8
eBook Packages: Computer ScienceComputer Science (R0)