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.

翻译：多领域推荐（MDR）近年来受到广泛关注，其利用来自多个领域的数据同时提升各领域的性能。然而，当前的MDR模型存在两个局限性。首先，大多数模型采用显式跨域参数共享的方法，导致领域间相互干扰。其次，由于领域间存在分布差异，现有方法中静态参数的使用限制了其适应不同领域的灵活性。为解决这些挑战，我们提出了一种新型模型——面向多领域推荐的超适配器（HAMUR）。具体而言，HAMUR由两个组件构成：（1）领域特定适配器，设计为可插拔模块，能无缝集成到现有多种多领域骨干模型中；（2）领域共享超网络，隐式捕获领域间的共享信息并动态生成适配器的参数。我们基于多种骨干网络在两个公开数据集上开展了大量实验，实验结果验证了所提模型的有效性和可扩展性。