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Using the Euclidean Distance for Retrieval Evaluation

Wu, Shengli, Bi, Yaxin and zeng, xiaoqin (2011) Using the Euclidean Distance for Retrieval Evaluation. In: Using the Euclidean Distance for Retrieval Evaluation. Springer Berlin Heidelberg. 14 pp. [Conference contribution]

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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.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25455
Deposited By: Dr Yaxin Bi
Deposited On:19 Jan 2016 09:35
Last Modified:19 Jan 2016 09:35

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