Info-flow Enhanced GANs for Recommender
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Summary
This paper proposes a new GAN model to enhance the information flow within the generator based on the Information flow between the original generator and discriminator, and results indicate that the model reduces the discrepancy between the generator and the discriminator.
- Type
- article
- Published
- 2021-07-11
- Cited by
- 6
- References
- 23
- OpenAlex
- https://openalex.org/W3156863413
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:235792562
Keywords
Discriminator, Generator (circuit theory), Computer science, Recommender system, Process (computing)
References
- Learning to Rank with Nonsmooth Cost Functions
- Learning to rank: from pairwise approach to listwise approach
- From RankNet to LambdaRank to LambdaMART: An Overview
- Learning to rank using gradient descent
- Learning Structured Output Representation using Deep Conditional Generative Models
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
- Collaborative Metric Learning
- Neural Collaborative Filtering
- IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
- Wasserstein Generative Adversarial Networks
- Long Text Generation via Adversarial Training with Leaked Information
- Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
- Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation
- CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation
- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
- Generative Adversarial Networks
Cited by
- Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm
- ALTRec: Adversarial Learning for Autoencoder-based Tail Recommendation
- Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks
- A Recommendation Algorithm Combining Local and Global Interest Features
- IGAN: A collaborative filtering model based on Improved Generative Adversarial Networks for recommendation
- DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model
- GenSim : GAN based Recommendation systems for personalized matrix factorization
- Creating synthetic datasets for collaborative filtering recommender systems using generative adversarial networks
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