Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation

TL;DR

This paper introduces H3Trans, a hierarchical hypergraph network that enhances multi-domain recommendation by transferring preferences across domains through a unified graph structure, addressing domain discrepancy and user preference scattering.

Contribution

The paper proposes a novel hierarchical hypergraph network with hyperedge modules for effective cross-domain preference transfer in multi-domain recommendation systems.

Findings

H3Trans outperforms existing methods on public datasets.

It effectively reduces domain discrepancy in item representations.

It improves user preference modeling by exploiting high-order connections.

Abstract

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.