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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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
|Deposited By:||Dr Shengli Wu|
|Deposited On:||19 Dec 2011 12:02|
|Last Modified:||19 Dec 2011 12:02|
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