ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2011.12566v1
• Date: Wed, 25 Nov 2020 08:10:35 GMT
• Title: ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks
• Authors: Po-Lin Lai, Chih-Yun Chen, Liang-Wei Lo, Chien-Chin Chen
• Abstract summary: We present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. Our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.
• Score: 0.1529342790344802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention of users to use a particular service. Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns. In this paper, we present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. The main idea of the proposed model is to train a network that learns the rating distributions of experienced users given their cold-start distributions. We further design a time-based function to restore the preferences of users to cold-start states. With extensive experiments on two real-world datasets, the results show that our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.

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