Journal
CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Volume -, Issue -, Pages 137-146Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3269206.3271743
Keywords
Top-N recommendation; collaborative filtering; generative adversarial networks; implicit feedback
Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIT: Ministry of Science and ICT) [NRF-2017R1A2B3004581]
- Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF-2017M3C4A7083678]
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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.
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