Abstract
In information retrieval systems and digital libraries, retrieval result evaluation is a very important aspect. Up to now, almost all commonly used metrics such as average precision and recall level precision are ranking based metrics. In this work, we investigate if it is a good option to use a score based method, the Euclidean distance, for retrieval evaluation. Two variations of it are discussed: one uses the linear model to estimate the relation between rank and relevance in resultant lists, and the other uses a more sophisticated cubic regression model for this. Our experiments with two groups of submitted results to TREC demonstrate that the introduced new metrics have strong correlation with ranking based metrics when we consider the average of all 50 queries. On the other hand, our experiments also show that one of the variations (the linear model) has better overall quality than all those ranking based metrics involved. Another surprising finding is that a commonly used metric, average precision, may not be as good as previously thought.
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)
Buckley, C., Voorhees, E.M.: Evaluating evaluation measure stability. In: Proceedings of ACM SIGIR Conference, Athens, Greece, pp. 33–40 (July 2000)
Calvé, A.L., Savoy, J.: Database merging strategy based on logistic regression. Information Processing & Management 36(3), 341–359 (2000)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 442–446 (2002)
Lee, J.H.: Analysis of multiple evidence combination. In: Proceedings of the 20th Annual International ACM SIGIR Conference, Philadelphia, Pennsylvania, USA, pp. 267–275 (July 1997)
Montague, M., Aslam, J.A.: Relevance score normalization for metasearch. In: Proceedings of ACM CIKM Conference, Berkeley, USA, pp. 427–433 (November 2001)
Sakai, T.: Evaluating evaluation metrics based on the bootstrap. In: Proceedings of ACM SIGIR Conference, Seattle, USA, pp. 525–532 (August 2006)
Sanderson, M., Zobel, J.: Information retrieval system evaluation: Effort, sensitivity, and reliability. In: Proceedings of ACM SIGIR Conference, Salvador, Brazil, pp. 162–169 (August 2005)
Wu, S., Bi, Y., McClean, S.: Regression relevance models for data fusion. In: Proceedings of the 18th International Workshop on Database and Expert Systems Applications, Regensburg, Germany, pp. 264–268 (September 2007)
Wu, S., Bi, Y., Zeng, X.: Retrieval result presentation and evaluation. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS, vol. 6291, pp. 125–136. Springer, Heidelberg (2010)
Wu, S., Crestani, F., Bi, Y.: Evaluating score normalization methods in data fusion. In: Ng, H.T., Leong, M.-K., Kan, M.-Y., Ji, D. (eds.) AIRS 2006. LNCS, vol. 4182, pp. 642–648. Springer, Heidelberg (2006)
Wu, S., McClean, S.: Evaluation of system measures for incomplete relevance judgment in IR. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds.) FQAS 2006. LNCS (LNAI), vol. 4027, pp. 245–256. Springer, Heidelberg (2006)
Zobel, J.: How reliable are the results of large-scale information retrieval experiments. In: Proceedings of ACM SIGIR Conference, Melbourne, Australia, pp. 307–314 (August 1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, S., Bi, Y., Zeng, X. (2011). Using the Euclidean Distance for Retrieval Evaluation. In: Fernandes, A.A.A., Gray, A.J.G., Belhajjame, K. (eds) Advances in Databases. BNCOD 2011. Lecture Notes in Computer Science, vol 7051. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24577-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-24577-0_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24576-3
Online ISBN: 978-3-642-24577-0
eBook Packages: Computer ScienceComputer Science (R0)