CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation

2026-08-10

AI Summary

In cross-domain sequential recommendation, existing approaches struggle to preserve the consistency of users’ domain-invariant interests due to their reliance on separate encoders and domain-wise loss aggregation. To address this limitation, this work proposes the CoRCi framework, which introduces a cross-reconstruction mechanism that leverages cross-attention to fuse domain-specific features and generate hybrid domain representations from domain-specific embeddings. Furthermore, it designs a FocalNCE loss function that extends InfoNCE by incorporating Focal Loss to intensify the penalty on intra-domain negative samples, thereby enabling unified sequence-level domain-agnostic optimization. Extensive experiments on four real-world datasets demonstrate that CoRCi significantly outperforms state-of-the-art baselines, achieving statistically significant improvements across all evaluation metrics.

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.

Problem

• Cross-Domain Sequential Recommendation
• Domain-Invariant Interests
• Interest Coherence
• Data Sparsity
• Inter-Domain Discrepancy

Innovation

• Cross-Reconstruction
• Domain-Invariant Interest
• FocalNCE
• Cross-Domain Sequential Recommendation
• Cross-Attention