提出一种新方法，用关键样本重放缓解推荐模型遗忘问题。
MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models
- 基于极值影响力评分选择关键样本重放。
- 在4个数据集上优于现有最佳方法，效果稳定且高效。
- 适用于多种推荐模型，适合动态推荐场景使用。
神经协同过滤模型广泛应用于推荐系统，但通常在静态数据分布下训练，难以适应用户偏好持续变化的动态环境。增量学习虽具潜力，但传统计算机视觉或自然语言处理方法因数据稀疏和任务差异，在推荐场景中表现受限。现有推荐模型增量学习方法仍有限且泛化能力弱。为此，我们提出MEGG：基于最大极端GGscore的体验重放框架。该方法引入GGscore这一新指标，量化样本影响程度，实现对高影响力样本的精准重放，有效缓解灾难性遗忘。MEGG具有模型无关性，可无缝集成至各类架构与框架。在三种神经推荐模型和四个基准数据集上的实验表明，其性能显著优于当前最优基线，具备良好可扩展性、效率与鲁棒性。代码将在论文被接收后公开。
原文摘要 · Abstract (English)
Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their applicability in dynamic environments where user preferences evolve. Incremental learning offers a promising solution, yet conventional methods from computer vision or NLP face challenges in recommendation tasks due to data sparsity and distinct task paradigms. Existing approaches for neural recommenders remain limited and often lack generalizability. To address this, we propose MEGG, Replay Samples with Maximally Extreme GGscore, an experience replay based incremental learning framework. MEGG introduces GGscore, a novel metric that quantifies sample influence, enabling the selective replay of highly influential samples to mitigate catastrophic forgetting. Being model-agnostic, MEGG integrates seamlessly across architectures and frameworks. Experiments on three neural models and four benchmark datasets show superior performance over state-of-the-art baselines, with strong scalability, efficiency, and robustness. Implementation will be released publicly upon acceptance.
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