Paper Info
Title
CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks.
Abstract
Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.
Year
DOI
Venue
2018
10.1145/3269206.3271743
CIKM
Keywords
Field
DocType
Top-N recommendation, collaborative filtering, generative adversarial networks, implicit feedback
Natural language generation,Data mining,Music generation,Image generation,Collaborative filtering,Computer science,Artificial intelligence,Generative grammar,Machine learning,Adversarial system
Conference
ISBN
Citations 
PageRank 
978-1-4503-6014-2
17
0.64
References 
Authors
26
4
Name
Order
Citations
PageRank
Dong-Kyu Chae15910.07
Jin-Soo Kang2221.04
Sang-Wook Kim3792152.77
Jungtae Lee422427.97