Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.

翻译：跨领域序列推荐旨在根据用户在多个领域中的历史序列交互预测未来交互行为。其核心挑战在于如何基于序列内与序列间项目交互挖掘精准的跨领域用户偏好。现有方法首先仅利用序列内项目交互学习单领域用户偏好，再构建迁移模块获取跨领域偏好。然而，这种流水线式隐式解决方案受限于迁移模块的瓶颈效应，且忽略了序列间项目关系。本文提出C^2DSR框架解决上述问题以捕获精准用户偏好，核心思想是同时利用序列内与序列间项目关系，联合学习单领域与跨领域用户偏好。具体而言，我们首先利用图神经网络挖掘序列间项目协同关系，再通过序列注意力编码器捕获序列内项目时序关系。在此基础上，设计两种不同的序列训练目标分别获取用户单领域与跨领域表征。此外，提出新型对比跨领域互信息最大化目标，通过最大化单领域与跨领域用户表征的互信息增强其关联性。为验证C^2DSR有效性，我们对四个电子商务数据集进行重新划分并开展大量实验，结果证明了本方法的有效性。