FairFS: Addressing Deep Feature Selection Biases for Recommender System

Large-scale online marketplaces and recommender systems serve as critical technological support for e-commerce development. In industrial recommender systems, features play vital roles as they carry information for downstream models. Accurate feature importance estimation is critical as it helps find the most useful feature subsets from thousands of feature candidates for online services. With such a selection, optimizing online performance while reducing computation burden is possible. To solve the feature selection problems of deep learning, trainable gate-based and sensitivitybased methods are proposed and proven effective in the industry. Nevertheless, by analyzing real-world examples, we identified three bias issues that make feature importance estimation rely on partial model layers, samples, or gradients to make decisions, ultimately leading to an inaccurate feature importance estimation. We call these biases layer bias, baseline bias, and approximation bias. To mitigate these three biases, we propose FairFS, a fair and accurate feature selection algorithm. On one hand, FairFS directly regularizes feature importance estimated across all non-linear transformational layers to avoid layer bias. On the other hand, it utilizes a smooth baseline feature that is close to the classifier's decision boundary and an aggregated approximation method to mitigate bias issues. Extensive experiments show how FairFS mitigates these three biases and achieves SOTA feature selection results. CCS Concepts • Information systems → Web applications.