轻量级剪枝框架提升推荐系统效率与精度平衡
Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems
- 用硬柯西分布预训练掩码层，快速识别关键特征
- 三阶段流程实现特征与模型联合剪枝，保持高精度
- 适合资源受限场景下的推荐系统优化，易部署
深度推荐系统（DRS）在处理高维输入特征时，常面临计算效率与模型精度之间的权衡。现有方法或侧重精度而忽略训练效率，或为追求效率牺牲多任务表现。本文提出 Light-FMP：一种轻量级特征与模型剪枝框架，包含预训练、剪枝和持续训练三个阶段。通过硬柯西分布与掩码层，在小规模数据子集上高效预训练以识别重要特征；随后对模型和特征进行剪枝，并在剩余数据集上以领域自适应参数继续训练。在真实推荐系统基准数据集上的实验表明，Light-FMP 在效率与精度上均优于现有方法，同时具备良好的可扩展性与鲁棒性。
原文摘要 · Abstract (English)
Deep recommender systems (DRS) often face challenges in balancing computational efficiency and model accuracy, especially when handling high-dimensional input features. Existing methods either focus on improving accuracy while neglecting training efficiency or prioritize efficiency at the cost of suboptimal accuracy across tasks. We propose Light-FMP: Lightweight Feature and Model Pruning for Enhanced DRS, a lightweight framework that addresses the challenges through three key phases: \textit{pretraining}, \textit{pruning}, and \textit{continued training}. Using a hard concrete distribution, a masking layer is efficiently pretrained on a small data subset to identify important features. The model and features are then pruned, and training continues on the remaining dataset with domain-adapted parameters. Experiments on benchmark datasets from real-world recommender systems demonstrate that Light-FMP outperforms existing methods in both efficiency and accuracy while maintaining scalability and robustness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。