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.

翻译：深度推荐系统在平衡计算效率与模型精度方面常面临挑战，尤其是在处理高维输入特征时。现有方法要么聚焦于提升精度而忽视训练效率，要么以牺牲跨任务精度为代价优先考虑效率。我们提出Light-FMP：一种面向增强型深度推荐系统的轻量级特征与模型剪枝框架，通过三个关键阶段解决上述挑战：\textit{预训练}、\textit{剪枝}和\textit{继续训练}。该方法利用硬性具体分布，在小规模数据子集上高效预训练掩码层以识别重要特征。随后对模型和特征进行剪剪枝，并基于域自适应参数在剩余数据集上继续训练。在来自真实推荐系统的基准数据集上的实验表明，Light-FMP在效率和精度上均优于现有方法，同时保持了可扩展性与鲁棒性。