  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.

Information Retrieval