HyMoERec用混合专家模型提升序列推荐效果
HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation
- 引入混合专家架构，分共享与专用专家处理用户行为差异
- 在MovieLens-1M和Beauty数据集上超越现有最佳模型
- 适合研究个性化推荐与模型可解释性的学者
我们提出HyMoERec，一种新型的序列推荐框架，解决了现有模型中统一的位置前馈网络带来的局限性。当前方法对所有用户交互和物品一视同仁，忽视了用户行为模式的异质性及物品复杂性的多样性。HyMoERec首次引入混合专家架构，结合共享与专用专家分支，并采用自适应专家融合机制，用于序列推荐任务。该设计能捕捉不同用户与物品的多样化推理过程，同时保障训练稳定性。在MovieLens-1M和Beauty数据集上的实验表明，HyMoERec始终优于现有最先进基线。
原文摘要 · Abstract (English)
We propose HyMoERec, a novel sequential recommendation framework that addresses the limitations of uniform Position-wise Feed-Forward Networks in existing models. Current approaches treat all user interactions and items equally, overlooking the heterogeneity in user behavior patterns and diversity in item complexity. HyMoERec initially introduces a hybrid mixture-of-experts architecture that combines shared and specialized expert branches with an adaptive expert fusion mechanism for the sequential recommendation task. This design captures diverse reasoning for varied users and items while ensuring stable training. Experiments on MovieLens-1M and Beauty datasets demonstrate that HyMoERec consistently outperforms state-of-the-art baselines.
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