Open AccessA collaborative filtering recommendation framework based on Wasserstein GAN
Author(s) -
Rui Li,
Fulan Qian,
X.-Y Du,
Shu Zhao,
Yanping Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012057
Subject(s) - collaborative filtering , computer science , recommender system , benchmark (surveying) , generator (circuit theory) , task (project management) , generative model , preference , data mining , artificial intelligence , machine learning , algorithm , generative grammar , mathematics , statistics , power (physics) , physics , management , geodesy , quantum mechanics , economics , geography
Compared with the original GAN, Wasserstein GAN minimizes the Wasserstein Distance between the generative distribution and the real distribution, can well capture the potential distribution of data and has achieved excellent results in image generation. However, the exploration of Wasserstein GAN on recommendation systems has received relatively less scrutiny. In this paper, we propose a collaborative filtering recommendation framework based on Wasserstein GAN called CFWGAN to improve recommendation accuracy. By learning the real user distribution, we can mine the potential nonlinear interactions between users and items, and capture users’ preferences for all items. Besides, we combine two positive and negative item sampling methods and add the reconstruction loss to the generator’s loss. This can well handle the problem of discrete data in recommendation (relative to the continuity of image data). By continuously approximating the generative distribution to the real user distribution, we can finally obtain better users’ preference information and provide higher accuracy in recommendation. We evaluate the CFWGAN model on three real-world datasets, and the empirical results show that our method is competitive with or superior to state-of-the-art approaches on the benchmark top-N recommendation task.