2006 · 80 citations · 8 references
ComputingComputer ScienceDatabasesInformation RetrievalText AnalyticsEvaluation Of Retrieval SystemsRetrieval ModelsSearch SystemsWeb SearchEvaluation Of Ranking ModelsInformation Retrieval EvaluationPrecisionRetrieval Effectiveness MeasuresSearch Ranking EvaluationSearch Result RankingTrec EvaluationTrec Evaluation Framework
There is a growing interest in estimating the effectiveness of search. Two approaches are typically considered: examining the search queries and examining the retrieved document sets. In this paper, we take the latter approach. We use four measures to characterize the retrieved document sets and estimate the quality of search. These measures are (i) the clustering tendency as measured by the Cox-Lewis statistic, (ii) the sensitivity to document perturbation, (iii) the sensitivity to query perturbation and (iv) the local intrinsic dimensionality. We present experimental results for the task of ranking 200 queries according to the search effectiveness over the TREC (discs 4 and 5) dataset. Our ranking of queries is compared with the ranking based on the average precision using the Kendall t statistic. The best individual estimator is the sensitivity to document perturbation and yields Kendall t of 0.521. When combined with the clustering tendency based on the Cox-Lewis statistic and the query perturbation measure, it results in Kendall t of 0.562 which to our knowledge is the highest correlation with the average precision reported to date.
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