Correcting for Position Bias in Learning to Rank: A Control Function Approach
This addresses a critical issue in information retrieval for improving ranking accuracy in systems like search engines, though it is an incremental advancement over existing bias correction methods.
The paper tackles the problem of position bias in learning-to-rank systems using implicit feedback, proposing a control function-based method that corrects for bias without requiring knowledge of the click model and allows for nonlinearity, with experimental results showing it outperforms state-of-the-art approaches.
Implicit feedback data, such as user clicks, is commonly used in learning-to-rank (LTR) systems because it is easy to collect and it often reflects user preferences. However, this data is prone to various biases, and training an LTR system directly on biased data can result in suboptimal ranking performance. One of the most prominent and well-studied biases in implicit feedback data is position bias, which occurs because users are more likely to interact with higher-ranked documents regardless of their true relevance. In this paper, we propose a novel control function-based method that accounts for position bias in a two-stage process. The first stage uses exogenous variation from the residuals of the ranking process to correct for position bias in the second stage click equation. Unlike previous position bias correction methods, our method does not require knowledge of the click or propensity model and allows for nonlinearity in the underlying ranking model. Moreover, our method is general and allows for debiasing any state-of-the-art ranking algorithm by plugging it into the second stage. We also introduce a technique to debias validation clicks for hyperparameter tuning to select the optimal model in the absence of unbiased validation data. Experimental results demonstrate that our method outperforms state-of-the-art approaches in correcting for position bias.