Variational bayes for modeling score distributions |
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| Authors: | Keshi Dai Evangelos Kanoulas Virgil Pavlu Javed A Aslam |
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| Institution: | (1) College of Computer and Information Science, Northeastern University, 360 Huntington Ave, #202 WVH, Boston, MA 02115, USA;(2) Department of Information Studies, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP, UK |
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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. |
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