Cross-domain recommendation (CDR) aims to leverage the correlation of users' behaviors in both the source and target domains to improve the user preference modeling in the target domain. Conventional CDR methods typically explore the dual-relations between the source and target domains' behaviors. However, this may ignore the informative mixed behaviors that naturally reflect the user's global preference. To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR. Specifically, Tri-CDR independently models the hidden representations for the triple behavior sequences and proposes a triple cross-domain attention (TCA) method to emphasize the informative knowledge related to both user's global and target-domain preference. To comprehensively explore the cross-domain correlations, we design a triple contrastive learning (TCL) strategy that simultaneously considers the coarse-grained similarities and fine-grained distinctions among the triple sequences, ensuring the alignment while preserving information diversity in multi-domain. We conduct extensive experiments and analyses on six cross-domain settings. The significant improvements of Tri-CDR with different sequential encoders verify its effectiveness and universality. The code will be released upon acceptance.

翻译：跨领域推荐（CDR）旨在利用用户在源域和目标域中行为的关联性，以改进目标域中的用户偏好建模。传统CDR方法通常探索源域与目标域行为之间的二元关系，但这可能忽略自然反映用户全局偏好的信息丰富混合行为。为解决该问题，我们提出一种新颖框架——跨领域推荐的三重序列学习（Tri-CDR），该框架联合建模源域、目标域及混合行为序列，以突出全局偏好与目标偏好，并精确建模CDR中的三重关联。具体而言，Tri-CDR独立建模三重行为序列的隐藏表示，并提出一种三重跨域注意力（TCA）方法，以强调与用户全局偏好及目标偏好相关的信息性知识。为全面探索跨领域关联，我们设计了一种三重对比学习（TCL）策略，该策略同时考虑三重序列间的粗粒度相似性与细粒度差异性，确保对齐的同时保持多领域信息多样性。我们在六种跨领域设置上进行了大量实验与分析。Tri-CDR结合不同序列编码器所取得的显著提升验证了其有效性与普适性。代码将在接收后公开。