论文标题
CNGAN：针对非重叠用户的跨网络用户偏好生成的生成对抗网络
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
论文作者
论文摘要
跨网络推荐解决方案的主要缺点是，它们只能应用于跨网络重叠的用户。因此，构成大多数用户的非裁决用户被忽略。作为解决方案，我们提出了CNGAN，这是一种基于多任务的新型学习，编码器 - 记者的体系结构。所提出的模型通过学习从目标到源网络首选项歧管的映射来为非重叠用户生成源网络用户偏好。最终的用户偏好用于基于暹罗网络的神经推荐体系结构。此外，我们提出了一种新型的基于用户的成对损耗功能，用于推荐使用隐式交互，以更好地指导多任务学习环境中的生成过程。我们通过在Twitter源网络上生成用户偏好来说明我们的解决方案，以获取YouTube目标网络上的建议。广泛的实验表明，生成的首选项可用于改善针对非重叠用户的建议。与最先进的跨网络推荐解决方案相比，最终的建议在准确性，新颖性和多样性方面取得了出色的性能。
A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel multi-task learning based, encoder-GAN-recommender architecture. The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds. The resultant user preferences are used in a Siamese network based neural recommender architecture. Furthermore, we propose a novel user based pairwise loss function for recommendations using implicit interactions to better guide the generation process in the multi-task learning environment.We illustrate our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network. Extensive experiments show that the generated preferences can be used to improve recommendations for non-overlapped users. The resultant recommendations achieve superior performance compared to the state-of-the-art cross-network recommender solutions in terms of accuracy, novelty and diversity.
