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Ranking Retrieval Systems with Partial Relevance Judgements

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wu, Shengli and Crestani, Fabio (2008) Ranking Retrieval Systems with Partial Relevance Judgements. Journal of Universal Computer Scicence , 14 (7). pp. 1020-1030. [Journal article]

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URL: http://www.jucs.org/jucs_14_7/ranking_retrieval_systems_with

DOI: 10.3217/jucs-014-07-1020

Abstract

Some measures such as mean average precision and recall level precision are considered as good system-oriented measures, because they concern both precision and recall that are two important aspects for effectiveness evaluation of information retrieval systems. However, such good system-oriented measures suffer from some shortcomings when partial relevance judgments are used. In this paper, we discuss how to rank retrieval systems in the condition of partial relevance judgments, which is common in major retrieval evaluation events such as TREC conferences and NTCIR workshops. Four system-oriented measures, which are mean average precision, recall level precision, normalized discount cumulative gain, and normalized average precision over all documents, are discussed. Our investigation shows that averaging values over a set of queries may not be the most reliable approach to rank a group of retrieval systems. Some alternatives such as Borda count, Condorcet voting, and the Zero-one normalization method, are investigated. Experimental results are also presented for the evaluation of these methods.

Item Type:Journal article
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:20640
Deposited By:Dr Shengli Wu
Deposited On:19 Dec 2011 12:02
Last Modified:19 Dec 2011 12:02

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