Advanced recommender systems usually involve multiple domains (scenarios or
categories) for various marketing strategies, and users interact with them to
satisfy their diverse demands. The goal of multi-domain recommendation 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 for serving 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. These
shortcomings greatly limit their performance in multi-domain recommendation.
To tackle the limitations, an appropriate way is to learn from multi-domain
user feedbacks and obtain separate user representations to characterize their
domain-specific preferences. In this paper we propose H3Trans, a
hierarchical hypergraph network based correlative preference transfer framework
for multi-domain recommendation. H3Trans represents multi-domain
feedbacks into a unified graph to help preference transfer via taking full
advantage of users' multi-domain behaviors. We incorporate two hyperedge-based
modules, namely dynamic item transfer module (Hyper-I) and adaptive user
aggregation module (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
among them to learn user representations. Experimental results on both public
datasets and large-scale production datasets verify the superiority of
H3Trans for multi-domain recommendation.Comment: Work in progres