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

arXiv: https://arxiv.org/abs/2011.12566 · HTML: https://mlsanity.com/paper/2011.12566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs](https://mlsanity.com/paper/2304.08968/) (2023)

## Abstract (arXiv)

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.