AutoField: Automating Feature Selection in Deep Recommender Systems
This work addresses the inefficiency and labor-intensive nature of feature selection for developers and researchers in recommender systems, offering an incremental improvement over existing methods.
The paper tackles the problem of manual or exhaustive feature selection in deep recommender systems by proposing an AutoML framework that automatically selects essential feature fields, resulting in reduced embedding parameters and inference time while maintaining recommendation performance across three benchmark datasets.
Feature quality has an impactful effect on recommendation performance. Thereby, feature selection is a critical process in developing deep learning-based recommender systems. Most existing deep recommender systems, however, focus on designing sophisticated neural networks, while neglecting the feature selection process. Typically, they just feed all possible features into their proposed deep architectures, or select important features manually by human experts. The former leads to non-trivial embedding parameters and extra inference time, while the latter requires plenty of expert knowledge and human labor effort. In this work, we propose an AutoML framework that can adaptively select the essential feature fields in an automatic manner. Specifically, we first design a differentiable controller network, which is capable of automatically adjusting the probability of selecting a particular feature field; then, only selected feature fields are utilized to retrain the deep recommendation model. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our framework. We conduct further experiments to investigate its properties, including the transferability, key components, and parameter sensitivity.