Variational bayes for modeling score distributions

Authors:
Keshi Dai;Evangelos Kanoulas;Virgil Pavlu;Javed A. Aslam
Affiliations:
College of Computer and Information Science, Northeastern University, Boston, USA 02115;Department of Information Studies, University of Sheffield, Sheffield, UK S1 4DP;College of Computer and Information Science, Northeastern University, Boston, USA 02115;College of Computer and Information Science, Northeastern University, Boston, USA 02115
Venue:
Information Retrieval
Year:
2011

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Abstract

Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications. In this work, we propose modeling the relevant documents' scores by a mixture of Gaussians and the non-relevant scores by a Gamma distribution. Applying Variational Bayes we automatically trade-off the goodness-of-fit with the complexity of the model. We test our model on traditional retrieval functions and actual search engines submitted to TREC. We demonstrate the utility of our model in inferring precision-recall curves. In all experiments our model outperforms the dominant exponential-Gaussian model.