Talks and Poster Presentations (with Proceedings-Entry):

A. Lipani, G. Zuccon, M. Lupu, B. Koopman, A. Hanbury:
"The Impact of Fixed-Cost Pooling Strategies on Test Collection Bias";
Talk: International Conference on the Theory of Information Retrieval, Padua, Italy; 2010-03-28 - 2010-03-31; in: "Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval", ACM, (2016), 105 - 108.

English abstract:
In Information Retrieval, test collections are usually built using the pooling method. Many pooling strategies have been developed for the pooling method. Herein, we address the question of identifying the best pooling strategy when evaluating systems using precision-oriented measures in presence of budget constraints on the number of documents to be evaluated. As a quality measurement we use the bias introduced by the pooling strategy, measured both in terms of Mean Absolute Error of the scores and in terms of ranking errors. Based on experiments on 15 test collections, we conclude that, for precision-oriented measures, the best strategies are based on Rank-Biased Precision (RBP). These results can inform collection builders because they suggest that, under fixed assessment budget constraints, RBP-based sampling produces less biased pools than other alternatives.

Pooling Method, Pooling Strategies, Pool Bias

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.