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arXiv · 2012.06901

GAN-based Recommendation with Positive-Unlabeled Sampling

Yao Zhou·Jianpeng Xu·Jun Wu·Zeinab Taghavi Nasrabadi·Evren Korpeoglu·Kannan Achan·Jingrui He
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Abstract

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.

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Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He. 2020-12-12. GAN-based Recommendation with Positive-Unlabeled Sampling. https://arxiv.org/abs/2012.06901

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