Entity linking is a new technique in recommender systems to link users’ interaction behaviors in different domains, for the purpose of improving the performance of the recommendation task. Linking-based cross-domain recommendation aims to alleviate the data sparse problem by utilizing the domain-sharable knowledge from auxiliary domains. However, existing methods fail to prevent domain-specific features to be transferred, resulting in suboptimal results. In this paper, we aim to address this issue by proposing an adversarial transfer learning based model ATLRec, which effectively captures domain-sharable features for cross-domain recommendation. In ATLRec, we leverage adversarial learning to generate representations of user-item interactions in both the source and the target domains, such that the discriminator cannot identify which domain they belong to, for the purpose of obtaining domain-sharable features. Meanwhile each domain learns its domain-specific features by a private feature extractor. The recommendation of each domain considers both domain-specific and domain-sharable features. We further adopt an attention mechanism to learn item latent factors of both domains by utilizing the shared users with interaction history, so that the representations of all items can be learned sufficiently in a shared space, even when few or even no items are shared by different domains. By this method, we can represent all items from the source and the target domains in a shared space, for the purpose of better linking items in different domains and capturing cross-domain item-item relatedness to facilitate the learning of domain-sharable knowledge. The proposed model is evaluated on various real-world datasets and demonstrated to outperform several state-of-the-art single-domain and cross-domain recommendation methods in terms of recommendation accuracy.

中文翻译：

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ATLRec：用于跨域推荐的注意力对抗迁移学习网络

实体链接是推荐系统中的一种新技术，用于链接用户在不同领域的交互行为，以提高推荐任务的性能。基于链接的跨域推荐旨在通过利用来自辅助域的域可共享知识来缓解数据稀疏问题。然而，现有的方法无法防止特定领域的特征被转移，导致结果不理想。在本文中，我们旨在通过提出一种基于对抗性迁移学习的模型 ATLRec 来解决这个问题，该模型有效地捕获了跨域推荐的域共享特征。在 ATLRec 中，我们利用对抗性学习在源域和目标域中生成用户-项目交互的表示，使得鉴别器无法识别它们属于哪个域，以获取域可共享的特征。同时，每个域通过私有特征提取器学习其特定于域的特征。每个域的推荐都考虑了特定于域和域可共享的特征。我们进一步采用了一种注意力机制，通过利用具有交互历史的共享用户来学习两个领域的项目潜在因素，以便在共享空间中充分学习所有项目的表示，即使不同项目共享很少甚至没有项目。域。通过这种方法，我们可以在共享空间中表示来自源域和目标域的所有项目，目的是更好地链接不同领域的items，捕捉跨领域item-item的相关性，方便领域共享知识的学习。所提出的模型在各种真实世界的数据集上进行了评估，并证明在推荐准确性方面优于几种最先进的单域和跨域推荐方法。