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 initially 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 specifically 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（Cross-domain Sequential Recommendation，CDSR）通过整合并学习多个域中不同粒度（从序列间到序列内、从单域到跨域）的交互信息，将用户偏好建模从平面提升至立体。本综述首先利用四维张量定义CDSR问题，进而分析其多维降维下的多类型输入表示。随后，我们从宏观与微观双重视角展开系统梳理：宏观层面，抽象跨域不同模型的多层级融合结构，并探讨其融合桥梁；微观层面，聚焦现有模型，具体阐述基础技术并解释辅助学习技术。最后，我们展示可用的公开数据集与代表性实验结果，并为CDSR未来研究方向提供见解。