  In recent years, dual-target Cross-Domain Recommendation (CDR) has been
proposed to capture comprehensive user preferences in order to ultimately
enhance the recommendation accuracy in both data-richer and data-sparser
domains simultaneously. However, in addition to users' true preferences, the
user-item interactions might also be affected by confounders (e.g., free
shipping, sales promotion). As a result, dual-target CDR has to meet two
challenges: (1) how to effectively decouple observed confounders, including
single-domain confounders and cross-domain confounders, and (2) how to preserve
the positive effects of observed confounders on predicted interactions, while
eliminating their negative effects on capturing comprehensive user preferences.
To address the above two challenges, we propose a Causal Deconfounding
framework via Confounder Disentanglement for dual-target Cross-Domain
Recommendation, called CD2CDR. In CD2CDR, we first propose a confounder
disentanglement module to effectively decouple observed single-domain and
cross-domain confounders. We then propose a causal deconfounding module to
preserve the positive effects of such observed confounders and eliminate their
negative effects via backdoor adjustment, thereby enhancing the recommendation
accuracy in each domain. Extensive experiments conducted on five real-world
datasets demonstrate that CD2CDR significantly outperforms the state-of-the-art
methods.
