C²DR: Robust Cross-Domain Recommendation based on Causal Disentanglement

Kong Menglin, Jia Wang*, Yushan Pan, Haiyang Zhang, Muzhou Hou

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Cross-domain recommendation aims to leverage heterogeneous information to transfers knowledge from a data-sufficient domain (source domain) to a data-scarce domain (target domain). Existing approaches mainly focus on learning single-domain user preferences and then employ a transferring module to obtain cross-domain user preferences, but ignore the modeling of users' domain specific preferences on items. We argue that incorporating domain-specific preferences from the source domain will introduce irrelevant information that fails to the target domain. Additionally, directly combining domain-shared and domain-specific information may hinder the target domain's performance. To this end, we propose C2DR, a novel approach that disentangles domain-shared and domain-specific preferences from a causal perspective. Specifically, we formulate a causal graph to capture the critical causal relationships based on the underlying recommendation process, explicitly identifying domain-shared and domain-specific information as causal irrelevant variables. Then, we introduce disentanglement regularization terms to learn distinct representations of the causal variables that obey the independence constraints in the causal graph. Remarkably, our proposed method enables effective intervention and transfer of domain-shared information, thereby improving the robustness of the recommendation model. We evaluate the efficacy of C2DR through extensive experiments on three real-world datasets, demonstrating significant improvements over state-of-The-Art baselines.

Original languageEnglish
Title of host publicationWSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages341-349
Number of pages9
ISBN (Electronic)9798400703713
DOIs
Publication statusPublished - 4 Mar 2024
Event17th ACM International Conference on Web Search and Data Mining, WSDM 2024 - Merida, Mexico
Duration: 4 Mar 20248 Mar 2024

Publication series

NameWSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Conference

Conference17th ACM International Conference on Web Search and Data Mining, WSDM 2024
Country/TerritoryMexico
CityMerida
Period4/03/248/03/24

Keywords

  • causal disentanglement
  • cross-domain recommendation
  • knowledge transfer

Fingerprint

Dive into the research topics of 'C²DR: Robust Cross-Domain Recommendation based on Causal Disentanglement'. Together they form a unique fingerprint.

Cite this