State-of-the-art Papers on GAN-based Recommender System (2017~2020) - designed by Jihoo Kim
- IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models (SIGIR)
- Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning (ACM Conference)
- GraphGAN: Graph Representation Learning with Generative Adversarial Nets (AAAI)
- Incorporating GAN for Negative Sampling in Knowledge Representation Learning (AAAI)
- Neural Memory Streaming Recommender Networks with Adversarial Training (KDD)
- RecGAN Recurrent Generative Adversarial Networks for Recommedation Systems (RecSys)
- A Knowledge-Enhanced Deep Recommendation Framework Incorporating GAN-based Models (ICDM)
- CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks (CIKM)
- PLASTIC Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training (IJCAI)
- Adversarial Point-of-Interest Recommendation (WWW)
- Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering (WWW)
- CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users (WWW)
- RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems (TNNLS)
- Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (ICML)
- Enhancing Collaborative Filtering with Generative Augmentation (KDD)
- c+GAN: Complementary Fashion Item Recommendation (KDD)
- PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation (IJCAI)
- DRCGR: Deep Reinforcement Learning Framework Incorporating CNN and GAN-Based for Interactive Recommendation (ICDM)
- Generating Reliable Friends via Adversarial Training to Improve Social Recommendation (ICDM)
- Adversarial Binary Collaborative Filtering for Implicit Feedback (AAAI)