论文标题
Coldgan：通过使用生成的对抗网络解决冷启动用户推荐
ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks
论文作者
论文摘要
在线服务提供商的推荐系统中，减轻新用户的冷启动问题至关重要，以影响用户在决策中的体验，这最终会影响用户使用特定服务的意图。先前的研究利用了用户和项目的各种辅助信息；但是，由于隐私问题，这可能是不切实际的。在本文中，我们提出了基于端到端的GAN模型Coldgan，无需使用侧面信息来解决此问题。拟议模型的主要思想是训练一个网络，鉴于他们的冷启动分布，了解经验丰富的用户的评级分布。我们进一步设计了一个基于时间的功能，以恢复用户对冷启动状态的偏好。通过对两个现实世界数据集进行了广泛的实验，结果表明，与最先进的推荐人相比，我们提出的方法可显着提高性能。
Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention of users to use a particular service. Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns. In this paper, we present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. The main idea of the proposed model is to train a network that learns the rating distributions of experienced users given their cold-start distributions. We further design a time-based function to restore the preferences of users to cold-start states. With extensive experiments on two real-world datasets, the results show that our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.
