Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we first define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we first discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.

翻译：跨域序列推荐（CDSR）通过整合并学习来自多个领域、不同粒度（从序列间到序列内、从单领域到跨领域）的交互信息，将用户偏好建模从平面层次提升至立体层次。本综述首先利用四维张量定义CDSR问题，进而分析其在多维降维条件下的多类型输入表示。随后，我们从宏观与微观两个视角进行系统梳理。宏观层面，我们抽象出各模型跨域的多层级融合结构并探讨其融合桥梁。微观层面，在现有模型基础上，我们首先讨论基础技术，继而阐释辅助学习技术。最后，我们展示可用的公开数据集与代表性实验结果，并对CDSR未来研究方向提出见解。