  Advanced recommender systems usually involve multiple domains (such as
scenarios or categories) for various marketing strategies, and users interact
with them to satisfy diverse demands. The goal of multi-domain recommendation
(MDR) is to improve the recommendation performance of all domains
simultaneously. Conventional graph neural network based methods usually deal
with each domain separately, or train a shared model to serve all domains. The
former fails to leverage users' cross-domain behaviors, making the behavior
sparseness issue a great obstacle. The latter learns shared user representation
with respect to all domains, which neglects users' domain-specific preferences.
In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network
based correlative preference transfer framework for MDR, which represents
multi-domain user-item interactions into a unified graph to help preference
transfer. $\mathsf{H^3Trans}$ incorporates two hyperedge-based modules, namely
dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U).
Hyper-I extracts correlative information from multi-domain user-item feedbacks
for eliminating domain discrepancy of item representations. Hyper-U aggregates
users' scattered preferences in multiple domains and further exploits the
high-order (not only pair-wise) connections to improve user representations.
Experiments on both public and production datasets verify the superiority of
$\mathsf{H^3Trans}$ for MDR.
