---
title: "A Survey on Cross-Domain Sequential Recommendation"
authors:
  - "Shu Chen"
  - "Zitao Xu"
  - "Weike Pan"
  - "Qiang Yang"
  - "Zhong Ming"
doi: "10.24963/ijcai.2024/884"
url: "https://ijcai.org/proceedings/2024/884"
pdf: "https://ijcai.org/proceedings/2024/0884.pdf"
venue: "Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence"
year: 2024
pages: "7989-7998"
track: "Survey Track"
keywords:
  - "Data Mining: DM: Recommender systems"
---

# A Survey on Cross-Domain Sequential Recommendation

Shu Chen, Zitao Xu, Weike Pan, Qiang Yang, Zhong Ming

*Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*, pages 7989-7998. DOI: [10.24963/ijcai.2024/884](https://doi.org/10.24963/ijcai.2024/884)

## Abstract

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.