论文标题
基于GAN的建议，未标记的采样
GAN-based Recommendation with Positive-Unlabeled Sampling
论文作者
论文摘要
推荐系统是在各种Web应用程序和个性化产品上进行信息检索任务的流行工具。在这项工作中，我们建议使用积极的未标记采样策略提出一个基于生成的对抗网络的推荐框架。具体来说，我们利用发电机来学习用户项目元组的连续分发，并将歧视器设计为二进制分类器，以在每个用户和每个项目之间输出相关得分。同时，在歧视者的学习过程中应用了阳性未标记的抽样。提供了有关歧视者和发电机的积极无标记的采样和收敛性最佳的理论界限。与13个受欢迎的基线相比，我们在三个公共访问数据集中显示了框架的有效性和效率。
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products. In this work, we propose a Generative Adversarial Network based recommendation framework using a positive-unlabeled sampling strategy. Specifically, we utilize the generator to learn the continuous distribution of user-item tuples and design the discriminator to be a binary classifier that outputs the relevance score between each user and each item. Meanwhile, positive-unlabeled sampling is applied in the learning procedure of the discriminator. Theoretical bounds regarding positive-unlabeled sampling and optimalities of convergence for the discriminators and the generators are provided. We show the effectiveness and efficiency of our framework on three publicly accessible data sets with eight ranking-based evaluation metrics in comparison with thirteen popular baselines.
