Title: EffSelect: Efficient Feature Value Selection for Deep Recommender Systems with Mini-Batch Training
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Feature Value Selection, Recommender Systems, CTR Prediction

Abstract: Features are critical to the performance of deep recommender systems, where they are typically represented as low-dimensional embeddings and fed into deep networks for prediction. However, a major challenge remains unaddressed: the sparsity and long-tail distribution in feature data result in a large number of non-informative feature values. These redundant values significantly increase memory usage and introduce noise, thereby impairing model performance. Most feature selection or pruning methods operate at a coarse granularity, either selecting entire features or fields, while finer-grained methods require a large number of additional learnable parameters. These methods struggle to effectively handle pervasive redundant features. To address these issues, we introduce EffSelect, a novel framework for finer-grained selection method at the level of feature values. Unlike previous methods, EffSelect directly quantifies the contribution to the prediction loss of each feature value as its importance. Specifically, we propose a mini-batch pre-training strategy that requires only 5% of the data for rapid warm-up, enabling real-time adaptation in dynamic systems. Using the trained model, we introduce an efficient and robust gradient-based mechanism to evaluate feature value contribution, discarding those features with low scores. EffSelect is theoretically guaranteed and achieves superior performance without introducing any additional learnable parameters to the base model. Extensive experiments on benchmark datasets validate the efficiency and effectiveness of EffSelect.

TL;DR: We proposed a efficient value selection framework for deep recommender systems.