# Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning

arXiv: https://arxiv.org/abs/1706.09200 · HTML: https://mlsanity.com/paper/1706.09200/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2017.

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

- [Deep Reinforcement Learning-Based Product Recommender for Online Advertising](https://mlsanity.com/paper/2102.00333/) (2021)

## Abstract (arXiv)

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.