Cross-domain recommendation (CDR) aims to leverage the users' behaviors in both source and target domains to improve the target domain's performance. Conventional CDR methods typically explore the dual relations between the source and target domains' behavior sequences. However, they ignore modeling the third sequence of mixed behaviors that naturally reflects the user's global preference. To address this issue, we present a novel and model-agnostic Triple sequence learning for cross-domain recommendation (Tri-CDR) framework to jointly model the source, target, and mixed behavior sequences in CDR. Specifically, Tri-CDR independently models the hidden user representations for the source, target, and mixed behavior sequences, and proposes a triple cross-domain attention (TCA) to emphasize the informative knowledge related to both user's target-domain preference and global interests in three sequences. To comprehensively learn the triple correlations, we design a novel triple contrastive learning (TCL) that jointly considers coarse-grained similarities and fine-grained distinctions among three sequences, ensuring the alignment while preserving the information diversity in multi-domain. We conduct extensive experiments and analyses on two real-world datasets with four domains. The significant improvements of Tri-CDR with different sequential encoders on all datasets verify the effectiveness and universality. The source code will be released in the future.

翻译：跨域推荐旨在利用用户在源域和目标域中的行为来提升目标域的性能。传统跨域推荐方法通常探索源域与目标域行为序列之间的双重关系，然而，它们忽略了建模能自然反映用户全局偏好的混合行为第三序列。为解决该问题，我们提出一种新颖且模型无关的跨域推荐三重序列学习框架，用于联合建模跨域推荐中的源域、目标域及混合行为序列。具体而言，Tri-CDR独立建模源域、目标域及混合行为序列的隐藏用户表征，并提出三重跨域注意力机制，以强调三个序列中与用户目标域偏好及全局兴趣相关的信息性知识。为全面学习三重关联，我们设计了一种新颖的三重对比学习，该学习联合考虑三个序列间的粗粒度相似性与细粒度差异性，在确保对齐的同时保留多域中的信息多样性。我们在包含四个领域的两个真实数据集上进行了广泛实验与分析。Tri-CDR在不同序列编码器下于所有数据集上的显著提升验证了其有效性与普适性。源代码将在未来公开。