While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due to the disjoint user and item groups across domains. In this paper, we propose a vector quantized meta learning for transferable sequential recommenders (MetaRec). Without requiring additional modalities or shared information across domains, our approach leverages user-item interactions from multiple source domains to improve the target domain performance. To solve the input heterogeneity issue, we adopt vector quantization that maps item embeddings from heterogeneous input spaces to a shared feature space. Moreover, our meta transfer paradigm exploits limited target data to guide the transfer of source domain knowledge to the target domain (i.e., learn to transfer). In addition, MetaRec adaptively transfers from multiple source tasks by rescaling meta gradients based on the source-target domain similarity, enabling selective learning to improve recommendation performance. To validate the effectiveness of our approach, we perform extensive experiments on benchmark datasets, where MetaRec consistently outperforms baseline methods by a considerable margin.

翻译：尽管序列推荐在捕捉用户-物品转移模式方面取得了显著进展，但由于跨领域用户和物品群体的不重叠性，迁移此类大规模推荐系统仍然具有挑战性。本文提出一种基于向量量化元学习的可迁移序列推荐方法（MetaRec）。该方法无需跨领域的额外模态信息或共享数据，通过利用多个源领域的用户-物品交互来提升目标领域的推荐性能。为解决输入异构性问题，我们采用向量量化技术，将来自异构输入空间的物品嵌入映射到共享特征空间。此外，我们的元迁移范式利用有限的目标领域数据来指导源领域知识向目标领域的迁移（即学习如何迁移）。同时，MetaRec通过基于源-目标领域相似度重新缩放元梯度，实现从多个源任务的自适应迁移，从而通过选择性学习提升推荐性能。为验证方法的有效性，我们在基准数据集上进行了大量实验，结果表明MetaRec始终以显著优势超越基线方法。