Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
arXiv:1706.09200
Abstract
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of user-preferred items. By recasting the energy function as the feature function, the proposed EB-SeqGANs is interpreted as an instance of maximum-entropy imitation learning.
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- How Generative Adversarial Networks and Their Variants Work: An Overview
- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
- CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
- Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
- Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
- Deep Trustworthy Knowledge Tracing
- Deep Reinforcement Learning-Based Product Recommender for Online Advertising