  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.
