Tri-Encoder Disentanglement with Dynamic Preference Gating for Cross-Domain Recommendation

Modeling user intent across heterogeneous domains remains a central challenge in cross-domain sequential recommendation (CDSR). Although cross-domain sequences naturally embed richer behavioral cues that traverse multiple domains, existing approaches often struggle to disentangle their distinctive effects from those of single-domain interactions. To address this limitation, this study proposes a novel user modeling framework that explicitly decomposes user preferences into three orthogonal components: domain-specific, domain-common, and cross-sequence-exclusive representations. The proposed method employs a triple-encoder architecture, where each encoder is specialized to capture one of these preference types. This is achieved through domain masking objectives and adversarial learning with a gradient reversal layer to promote balanced separation. In addition, representational alignment and mutual independence are jointly enforced to achieve a clean disentanglement among the components. To further enhance temporal adaptability, a dynamic gating mechanism is incorporated to modulate the relative influence of each preference type at every timestep, culminating in a unified session-level representation. Extensive experiments conducted on two large-scale real-world benchmarks confirm that the proposed approach not only enhances recommendation accuracy but also yields interpretable insights into the interplay between domain-specific and cross-domain preference signals.