GAN-based Recommendation with Positive-Unlabeled Sampling
This work provides an incremental improvement in recommender systems for users of web applications and personalized products.
This paper proposes a GAN-based recommendation framework that uses a positive-unlabeled sampling strategy to address information retrieval tasks. The framework demonstrates effectiveness and efficiency on three public datasets, outperforming thirteen popular baselines across eight ranking-based evaluation metrics.
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.