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Forward and Backward Feature Selection for Query Performance Prediction

Déjean, Sebastien and Ionescu, Radu Tudor and Mothe, Josiane and Ullah, Md Zia Forward and Backward Feature Selection for Query Performance Prediction. (2020) In: 35th Annual ACM Symposium on Applied Computing (SAC 2020), 30 March 2020 - 3 April 2020 (Brno, Czech Republic).

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Official URL: https://doi.org/10.1145/3341105.3373904

Abstract

The goal of query performance prediction (QPP) is to automatically estimate the effectiveness of a search result for any given query, without relevance judgements. Post-retrieval features have been shown to be more effective for this task while being more expensive to compute than pre-retrieval features. Combining multiple post-retrieval features is even more effective, but state-of-the-art QPP methods are impossible to interpret because of the black-box nature of the employed machine learning models. However, interpretation is useful for understanding the predictive model and providing more answers about its behavior. Moreover, combining many post-retrieval features is not applicable to real-world cases, since the query running time is of utter importance. In this paper, we investigate a new framework for feature selection in which the trained model explains well the prediction. We introduce a step-wise (forward and backward) model selection approach where different subsets of query features are used to fit different models from which the system selects the best one. We evaluate our approach on four TREC collections using standard QPP features. We also develop two QPP features to address the issue of query-drift in the query feedback setting. We found that: (1) our model based on a limited number of selected features is as good as more complex models for QPP and better than non-selective models; (2) our model is more efficient than complex models during inference time since it requires fewer features; (3) the predictive model is readable and understandable; and (4) one of our new QPP features is consistently selected across different collections, proving its usefulness.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to ACM (Association for Computing Machinery). The definitive version is available at http://dl.acm.org This papers appears in SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing. The original PDF is available at: https://dl.acm.org/doi/abs/10.1145/3341105.3373904
HAL Id:hal-02942304
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > University of Bucharest (ROMANIA)
Laboratory name:
Funders:
European Union’s Horizon 2020 H2020-SU-SEC-2018 (Europe)
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Deposited On:26 Aug 2020 10:13

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