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.

翻译：我们提出HyMoERec，一种新颖的序列推荐框架，旨在解决现有模型中统一位置前馈网络的局限性。当前方法对所有用户交互和物品进行同等处理，忽视了用户行为模式的异质性与物品复杂度的多样性。HyMoERec首次为序列推荐任务引入了一种混合专家混合架构，该架构结合了共享与专用专家分支，并配备自适应专家融合机制。该设计能够捕捉不同用户和物品的多样化推理模式，同时确保训练稳定性。在MovieLens-1M和Beauty数据集上的实验表明，HyMoERec在各项指标上均持续优于现有最先进的基线模型。