Core Concepts
Effective feature selection methods are crucial for enhancing accuracy and optimizing storage efficiencies in Deep Recommender Systems.
Abstract
This research paper introduces ERASE, a comprehensive benchmark for feature selection methods in Deep Recommender Systems (DRS). The study addresses challenges in experimental setups, lack of detailed analysis on selection attributes, and the focus on peak performance rather than robustness. ERASE evaluates eleven feature selection methods across various datasets, emphasizing fair comparisons and robustness assessment. The paper also introduces a novel metric, AUKC, to evaluate the stability of feature selection methods across different numbers of selected features. Experimental results demonstrate the effectiveness and efficiency of different methods in reducing memory usage while maintaining model accuracy. Online experiments validate the practical utility of the benchmark toolkit.
Abstract:
Introduction to Deep Recommender Systems (DRS) and the importance of feature selection.
Challenges faced in existing research regarding unfair comparisons, lack of detailed analysis, and focus on peak performance.
Introduction to ERASE as a comprehensive benchmark for evaluating feature selection methods in DRS.
Description of evaluation methodology including fair comparisons, robustness assessment, and introduction of AUKC metric.
Results from experiments showcasing effectiveness, efficiency, memory-saving capabilities, and alignment with industrial datasets.
Conclusion highlighting the significance of ERASE as a valuable tool for guiding future research in DRS feature selection.
Experiments:
Evaluation of eleven feature selection methods across various datasets.
Assessment of robustness and stability using AUKC metric.
Analysis of memory-saving capabilities under performance limitations.
Comparison between public datasets and large-scale industrial datasets.
Online experiments validating practical utility.
Related Works:
Overview of traditional feature selection methods: filter, wrapper, embedded.
Review of existing benchmarks focusing on downstream models or synthetic data.
Comparison with related work like DeepLasso and its limitations compared to ERASE.
Stats
"Effective feature selection methods are crucial for enhancing accuracy."
"ERASE comprises a thorough evaluation of eleven feature selection methods."
"AUKC metric designed to assess feature selection efficacy comprehensively."
Quotes
"Focusing on peak performance is computationally infeasible."
"Gate-based methods show better performance compared to shallow features."
"AutoField significantly surpasses other gate-based feature selection methods."