CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation
2026-08-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors study how to improve recommendation systems that suggest items based on user behavior across multiple related categories (domains). They point out that existing methods struggle to balance domain-specific and shared user interests, often treating data from different domains separately. Their new method, CoRCi, combines information from multiple domains more smoothly by using a special cross-attention mechanism and a novel loss function that encourages learning common patterns while reducing domain differences. They tested CoRCi on several datasets and found it consistently works better than previous approaches.
Cross-Domain Sequential RecommendationData SparsityDomain-Invariant InterestsCross-AttentionSeq2Seq ModelingInfoNCE LossFocal LossRepresentation LearningRecommendation Systems
Authors
Qingtian Bian, Tieying Li, Marcus de Carvalho, Jiaxing Xu, Hui Fang, Yiping Ke
Abstract
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.