HAMUR: Hyper Adapter for Multi-Domain Recommendation

TL;DR

HAMUR introduces a flexible hyper network approach with domain-specific adapters for multi-domain recommendation, effectively addressing interference and adaptability issues in existing models.

Contribution

The paper proposes HAMUR, a novel hyper network-based model with domain-specific adapters and shared hyper-networks, improving multi-domain recommendation performance and flexibility.

Findings

HAMUR outperforms existing models on two public datasets.

The model demonstrates high scalability and adaptability across different backbone networks.

Experimental results validate the effectiveness of the proposed approach.

Abstract

Multi-Domain Recommendation (MDR) has gained significant attention in recent years, which leverages data from multiple domains to enhance their performance concurrently.However, current MDR models are confronted with two limitations. Firstly, the majority of these models adopt an approach that explicitly shares parameters between domains, leading to mutual interference among them. Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains. To address these challenges, we propose a novel model Hyper Adapter for Multi-Domain Recommendation (HAMUR). Specifically, HAMUR consists of two components: (1). Domain-specific adapter, designed as a pluggable module that can be seamlessly integrated into various existing multi-domain backbone models, and (2). Domain-shared hyper-network, which implicitly captures shared information among domains and dynamically generates the parameters for the adapter. We conduct extensive experiments on two public datasets using various backbone networks. The experimental results validate the effectiveness and scalability of the proposed model.