Multi-Domain Enhancement via Residual Interwoven Transfer in Cross-Domain Sequential Recommendation

To mitigate data sparsity in Sequential Recommendation, Cross-Domain Sequential Recommendation (CDSR) exploits dynamic knowledge transfer across domains. Traditional CDSR approaches merge specific-domain sequences into mixed-domain sequences to reconnect users' dispersed interests. However, most methods rely on unidirectional transfer between mixed and specific domains on each domain task, overlooking the complex interplay between mixed-domain and domain-specific dynamics. Moreover, token-level transfer between coinciding domain sequences fails to consider inherent sequential dynamics. To address these limitations, we propose Multi-Domain Enhancement via Residual Interwoven Transfer (MERIT). Specifically, MERIT enhances domain representations along multiple domain-to-domain paths, leveraging the proposed extended cross-attention fusion compatible with partially overlapping sequences. To facilitate such transfers, MERIT further employs MoE networks in encoders to generate both intra-domain and inter-domain representations. In addition, by integrating stopped-gradient mixed-domain representations into specific-domain representations, MERIT enables the model to learn the residual signal of the mixed-domain information, better aligning with downstream specific-domain tasks. Extensive experiments on three real-world datasets demonstrate that MERIT consistently outperforms state-of-the-art CDSR counterparts with statistical significance.