Modeling the Score Distributions of Relevant and Non-relevant Documents
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- Kanoulas E., Pavlu V., Dai K., Aslam J.A. (2009) Modeling the Score Distributions of Relevant and Non-relevant Documents. In: Azzopardi L. et al. (eds) Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg
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 modeling the non-relevant scores by a Gamma distribution. Applying variational inference 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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