Recommendation System Based on Users Preference Mining Generative Adversarial Networks

Abstract

Users preference mining is one of the key issues in the research field of recommendation system, and it plays a very important role in improving the recommendation performance. Users preference mining generative adversarial networks (UPM-GAN) is proposed to better analyze the implicit users preference in the recommendation procedure from two aspects. On one hand, user-rating matrix is processed by the state-of-the-art triplet loss algorithm. It means better positive samples are obtained by the hard-negative mining procedure of the triplet loss algorithm, which will build a strong foundation for more accurately portraying users’ preference. On the other hand, SVD++ algorithm is utilized in turn to create the generation model of the UPM-GAN. The SVD++ algorithm can mine implicit users preference by adding bias information and latent parameters. It helps improve the rating prediction accuracy of recommendation system. Finally, the state-of-the-art GAN framework is utilized to train the proposed recommendation system and experimental simulation is completed on two mainstream datasets: MovieLens-100K and MovieLens-1M. Experimental results demonstrate that the proposed UPM-GAN is superior to other baselines among all evaluation indices including Precision@K, mean average precision (MAP). Moreover, it has the advantages of faster convergence speed and stable training process. The proposed recommendation system based on UPM-GAN has very large practical value.

Keywords

• recommendation systems
• generative adversarial networks (gan)
• users preference mining
• singular value decomposition (svd++)
• triplet loss
• hard negative samples