Outlier data points are known to affect negatively the learning process of regression or classification models, yet their impact in the learning-to-rank scenario has not been thoroughly investigated so far. In this work we propose SOUR, a learning-to-rank method that detects and removes outliers before building an effective ranking model. We limit our analysis to gradient boosting decision trees, where SOUR searches for outlier instances that are incorrectly ranked in several iterations of the learning process. Extensive experiments show that removing a limited number of outlier data instances before re-training a new model provides statistically significant improvements, and that SOUR outperforms state-of-the-art de-noising and outlier detection methods.

Filtering out Outliers in Learning to Rank

Marcuzzi F.;Lucchese C.;Orlando S.
2022

Abstract

Outlier data points are known to affect negatively the learning process of regression or classification models, yet their impact in the learning-to-rank scenario has not been thoroughly investigated so far. In this work we propose SOUR, a learning-to-rank method that detects and removes outliers before building an effective ranking model. We limit our analysis to gradient boosting decision trees, where SOUR searches for outlier instances that are incorrectly ranked in several iterations of the learning process. Extensive experiments show that removing a limited number of outlier data instances before re-training a new model provides statistically significant improvements, and that SOUR outperforms state-of-the-art de-noising and outlier detection methods.
ICTIR 2022 - Proceedings of the 2022 ACM SIGIR International Conference on the Theory of Information Retrieval
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5004960
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