AEFS: Adaptive Early Feature Selection for Deep Recommender Systems

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Fan Hu, Gaofeng Lu, Jun Chen, Chaonan Guo, Yuekui Yang, Xirong Li

Published: 2025, Last Modified: 27 Feb 2026IEEE Trans. Knowl. Data Eng. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The quality of features plays an important role in the performance of recommender systems. Recognizing this, feature selection has emerged as a crucial technique in refining recommender systems. Recent advancements leveraging Automated Machine Learning (AutoML) has drawn significant attention, particularly in two main categories: early feature selection and late feature selection, differentiated by whether the selection occurs before or after the embedding layer. The early feature selection selects a fixed subset of features and retrains the model, while the late feature selection, known as adaptive feature selection, dynamically adjusts feature choices for each data instance, recognizing the variability in feature significance. Although adaptive feature selection has shown remarkable improvements in performance, its main drawback lies in its post-embedding layer feature selection. This process often becomes cumbersome and inefficient in large-scale recommender systems with billions of ID-type features, leading to a highly sparse and parameter-heavy embedding layer. To overcome this, we introduce Adaptive Early Feature Selection(AEFS), a very simple method that not only adaptively selects informative features for each instance, but also significantly reduces the activated parameters of the embedding layer. AEFSemploys a dual-model architecture, encompassing an auxiliary modeldedicated to feature selection and a main modelresponsible for prediction. To ensure effective alignment between these two models, we incorporate two collaborative training loss constraints. Our extensive experiments on three benchmark datasets validate the efficiency and effectiveness of our approach. Notably, AEFSmatches the performance of current state-of-the-art Adaptive Late Feature Selection methods while achieving a significant reduction of 37. 5% in the activated parameters of the embedding layer. We believe that this work opens up new possibilities for feature selection.