Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains. While significant endeavors have been made, they primarily concentrated on developing advanced transfer modules and aligning user representations using self-supervised learning techniques. However, the problem of aligning item representations has received limited attention, and misaligned item representations can potentially lead to sub-optimal sequential modeling and user representation alignment. To this end, we propose a model-agnostic framework called \textbf{C}ross-domain item representation \textbf{A}lignment for \textbf{C}ross-\textbf{D}omain \textbf{S}equential \textbf{R}ecommendation (\textbf{CA-CDSR}), which achieves sequence-aware generation and adaptively partial alignment for item representations. Specifically, we first develop a sequence-aware feature augmentation strategy, which captures both collaborative and sequential item correlations, thus facilitating holistic item representation generation. Next, we conduct an empirical study to investigate the partial representation alignment problem from a spectrum perspective. It motivates us to devise an adaptive spectrum filter, achieving partial alignment adaptively. Furthermore, the aligned item representations can be fed into different sequential encoders to obtain user representations. The entire framework is optimized in a multi-task learning paradigm with an annealing strategy. Extensive experiments have demonstrated that CA-CDSR can surpass state-of-the-art baselines by a significant margin and can effectively align items in representation spaces to enhance performance.

翻译：跨域序列推荐（CDSR）旨在发掘并迁移用户在多个推荐域中的序列偏好。尽管已有大量研究，但它们主要集中于开发先进的迁移模块，并利用自监督学习技术对齐用户表示。然而，物品表示的对齐问题尚未得到充分关注，而未对齐的物品表示可能导致次优的序列建模与用户表示对齐。为此，我们提出一个与模型无关的框架——面向跨域序列推荐的跨域物品表示对齐（CA-CDSR），该框架实现了序列感知的生成与物品表示的自适应部分对齐。具体而言，我们首先提出一种序列感知的特征增强策略，该策略同时捕捉协同与序列层面的物品关联，从而促进整体物品表示的生成。接着，我们通过实证研究从频谱视角探究部分表示对齐问题，这启发我们设计一种自适应频谱滤波器，以实现自适应部分对齐。此外，对齐后的物品表示可输入不同的序列编码器以获得用户表示。整个框架采用退火策略的多任务学习范式进行优化。大量实验表明，CA-CDSR能够显著超越现有最优基线方法，并能在表示空间中有效对齐物品以提升推荐性能。