发表机构
Nanyang Technological University; Northeastern University; Shanghai University of Finance and Economics（南洋理工大学; 东北大学; 上海财经大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对跨域序列推荐中域差异破坏兴趣连贯性的问题，本文提出CoRCi框架，通过跨重建方法和FocalNCE损失实现域不变兴趣对齐，在四个数据集上性能优于现有最优方法。
AI 中文摘要
跨域序列推荐（CDSR）旨在通过在相关域间传递动态用户兴趣来缓解数据稀疏性问题，其核心挑战在于有效桥接不同域。单域建模无法区分域特定兴趣与域不变兴趣；现有方法会按时间顺序将域特定序列合并为混合域序列以捕获域不变知识，但通常为混合域序列部署独立编码器，并按域聚合损失，该流程会放大域间差异、破坏域不变兴趣的连贯性，尤其当序列到序列（Seq2Seq）中的查询-目标对来自不同域时问题更突出。本文提出双目标CDSR框架CoRCi（连贯兴趣的跨重建）以解决上述缺陷：其提出跨重建方法，通过交叉注意力从预编码的特定域表示直接生成混合域表示，再用单一的、序列级的、域无关损失训练，以保留域不变兴趣的连贯性；为进一步抑制混合域建模中的域差异，CoRCi引入FocalNCE，将Focal Loss嵌入混合域InfoNCE目标，该新损失对与查询同域的负样本施加更高惩罚，从而强化域不变对齐。在四个真实数据集上的大量实验表明，CoRCi在所有指标上均显著优于现有最优CDSR方法，取得具有统计显著性的提升。
英文摘要
Cross-Domain Sequential Recommendation (CDSR) aims to alleviate data sparsity by transferring dynamic user interests across related domains. A key challenge lies in effectively bridging these domains. In single-domain modeling, models cannot distinguish between domain-specific and domain-invariant interests. Recent methods merge domain-specific sequences chronologically into a mixed-domain sequence to capture domain-invariant knowledge. However, they typically deploy separate encoders for the mixed-domain sequence and train them with per-domain loss aggregation. This workflow magnifies inter-domain discrepancies and disrupts domain-invariant interest coherence, especially when query target pairs in Seq2Seq originate from different domains. In this paper, we present CoRCi (Cross-Reconstruction for Coherent Interest), a dual-target CDSR framework that tackles these drawbacks. Specifically, CoRCi proposes a Cross-Reconstruction approach that generates mixed-domain representations directly from pre-encoded specific-domain representations via cross-attention. The generated representations are then trained using a single, sequence-level, domain-agnostic loss to preserve the coherence of domain-invariant interests. To further suppress domain discrepancies in mixed-domain modeling, CoRCi introduces FocalNCE, which embeds Focal Loss into the preceding mixed-domain InfoNCE objective. The new loss assigns higher penalties to negatives drawn from the same domain as the query, thereby strengthening domain-invariant alignment. Extensive experiments on four real-world datasets demonstrate that CoRCi consistently outperforms state-of-the-art CDSR counterparts, achieving statistically significant gains across all metrics.
