In recommendation systems, how to effectively scale up recommendation models has been an essential research topic. While significant progress has been made in developing advanced and scalable architectures for sequential recommendation(SR) models, there are still challenges due to items' multi-faceted characteristics and dynamic item relevance in the user context. To address these issues, we propose Fuxi-MME, a framework that integrates a multi-embedding strategy with a Mixture-of-Experts (MoE) architecture. Specifically, to efficiently capture diverse item characteristics in a decoupled manner, we decompose the conventional single embedding matrix into several lower-dimensional embedding matrices. Additionally, by substituting relevant parameters in the Fuxi Block with an MoE layer, our model achieves adaptive and specialized transformation of the enriched representations. Empirical results on public datasets show that our proposed framework outperforms several competitive baselines.

翻译：在推荐系统中，如何有效扩展推荐模型一直是一个核心研究课题。尽管在开发先进且可扩展的序列推荐模型架构方面取得了显著进展，但由于物品的多面性特征及用户上下文中的动态物品相关性，仍存在诸多挑战。为解决这些问题，我们提出了Fuxi-MME框架，该框架将多嵌入策略与专家混合架构相结合。具体而言，为以解耦方式高效捕捉多样化的物品特征，我们将传统的单一嵌入矩阵分解为多个低维嵌入矩阵。此外，通过将Fuxi Block中的相关参数替换为MoE层，我们的模型实现了对丰富表征的自适应与专业化转换。在公开数据集上的实证结果表明，所提出的框架优于多个竞争性基线模型。