  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.
