Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems. This paper addresses those problems by leveraging the concepts which derive from representation learning, adversarial learning and transfer learning (particularly, domain adaptation). Although various transfer learning methods have shown promising performance in this context, our proposed novel method RecSys-DAN focuses on alleviating the cross-domain and within-domain data sparsity and data imbalance and learns transferable latent representations for users, items and their interactions. Different from existing approaches, the proposed method transfers the latent representations from a source domain to a target domain in an adversarial way. The mapping functions in the target domain are learned by playing a min-max game with an adversarial loss, aiming to generate domain indistinguishable representations for a discriminator. Four neural architectural instances of ResSys-DAN are proposed and explored. Empirical results on real-world Amazon data show that, even without using labeled data (i.e., ratings) in the target domain, RecSys-DAN achieves competitive performance as compared to the state-of-the-art supervised methods. More importantly, RecSys-DAN is highly flexible to both unimodal and multimodal scenarios, and thus it is more robust to the cold-start recommendation which is difficult for previous methods.
翻译:数据广度和数据不平衡是跨域建议系统的实际和具有挑战性的问题。本文件通过利用代表性学习、对抗性学习和转让学习(特别是领域适应)产生的概念来解决这些问题。虽然各种转让学习方法在这方面表现出有良好的表现,但我们提议的新方法RecSys-Dan侧重于缓解跨域和内域数据广度和数据不平衡,并学习用户、项目及其互动的可转让潜在代表形式。与现有方法不同,拟议方法以对抗方式将潜在代表形式从源域转移到目标域。目标域的绘图功能通过以对抗性损失来玩小麦游戏来学习,目的是为歧视者产生互不相容的表达方式。提议和探讨四个ResSys-Dan的神经建筑实例。现实世界亚马孙数据的积极结果显示,即使不使用目标域的标签数据(即评级),RESy-DAR也实现了与州域不同的竞争性表现,因此,最先开始的灵活和最灵活的方式是前期的灵活的方式。