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Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?

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Advances in Information Retrieval (ECIR 2024)

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Abstract

Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are much less interpretable. That is, it is very difficult to understand their inner workings and answer questions like how do they make their ranking decisions? or what document features do they find important? This is particularly disadvantageous since interpretability is highly important for real-world systems. In this work, we explore feature selection for neural learning-to-rank (LTR). In particular, we investigate six widely-used methods from the field of interpretable machine learning (ML) and introduce our own modification, to select the input features that are most important to the ranking behavior. To understand whether these methods are useful for practitioners, we further study whether they contribute to efficiency enhancement. Our experimental results reveal a large feature redundancy in several LTR benchmarks: the local selection method TabNet can achieve optimal ranking performance with less than 10 features; the global methods, particularly our G-L2x, require slightly more selected features, but exhibit higher potential in improving efficiency. We hope that our analysis of these feature selection methods will bring the fields of interpretable ML and LTR closer together.

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Notes

  1. 1.

    MQ2008 is omitted from cost analysis since no associated cost information is available.

  2. 2.

    https://github.com/dreamquark-ai/tabnet; https://github.com/lasso-net/lassonet.

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Acknowledgements

This work is partially supported by German Research Foundation (DFG), under the Project IREM with grant No. AN 996/1-1, and by the Netherlands Organisation for Scientific Research (NWO) under grant No. VI.Veni.222.269.

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  1. Delft University of Technology, Delft, The Netherlands

    Lijun Lyu, Nirmal Roy & Avishek Anand

  2. Radboud University, Nijmegen, The Netherlands

    Harrie Oosterhuis

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  3. Harrie Oosterhuis
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  4. Avishek Anand
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Correspondence to Lijun Lyu .

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Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Lyu, L., Roy, N., Oosterhuis, H., Anand, A. (2024). Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14611. Springer, Cham. https://doi.org/10.1007/978-3-031-56066-8_29

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  • DOI: https://doi.org/10.1007/978-3-031-56066-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56065-1

  • Online ISBN: 978-3-031-56066-8

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