Cross-domain recommendation (CDR) aims at leveraging 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 source code is available at https://github.com/hulkima/Tri-CDR.

跨域推荐（CDR）旨在利用源域和目标域中用户行为的相关性来改进目标域中的用户偏好建模。传统的 CDR 方法通常探索源域和目标域行为之间的双重关系。然而，这可能会忽略自然反映用户全局偏好的信息丰富的混合行为。为了解决这个问题，我们提出了一种新颖的框架，称为跨域推荐的三重序列学习（Tri-CDR），它联合建模源、目标和混合行为序列，以突出全局和目标偏好并精确建模三元组CDR 中的相关性。具体来说，Tri-CDR 独立地对三重行为序列的隐藏表示进行建模，并提出了三重跨域注意（TCA）方法来强调与用户全局和目标域偏好相关的信息知识。为了全面探索跨域相关性，我们设计了三重对比学习（TCL）策略，该策略同时考虑三重序列之间的粗粒度相似性和细粒度区别，在保证多域对齐的同时保留信息多样性。我们对六种跨域设置进行了广泛的实验和分析。 Tri-CDR与不同顺序编码器的显着改进验证了其有效性和通用性。源代码可在 https://github.com/hulkima/Tri-CDR 获取。