  Deep learning-based recommender systems may lead to over-fitting when lacking
training interaction data. This over-fitting significantly degrades
recommendation performances. To address this data sparsity problem,
cross-domain recommender systems (CDRSs) exploit the data from an auxiliary
source domain to facilitate the recommendation on the sparse target domain.
Most existing CDRSs rely on overlapping users or items to connect domains and
transfer knowledge. However, matching users is an arduous task and may involve
privacy issues when data comes from different companies, resulting in a limited
application for the above CDRSs. Some studies develop CDRSs that require no
overlapping users and items by transferring learned user interaction patterns.
However, they ignore the bias in user interaction patterns between domains and
hence suffer from an inferior performance compared with single-domain
recommender systems. In this paper, based on the above findings, we propose a
novel CDRS, namely semantic clustering enhanced debiasing graph neural
recommender system (SCDGN), that requires no overlapping users and items and
can handle the domain bias. More precisely, SCDGN semantically clusters items
from both domains and constructs a cross-domain bipartite graph generated from
item clusters and users. Then, the knowledge is transferred via this
cross-domain user-cluster graph from the source to the target. Furthermore, we
design a debiasing graph convolutional layer for SCDGN to extract unbiased
structural knowledge from the cross-domain user-cluster graph. Our Experimental
results on three public datasets and a pair of proprietary datasets verify the
effectiveness of SCDGN over state-of-the-art models in terms of cross-domain
recommendations.
