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.
@article{arxiv.1706.09200,
title = {Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning},
author = {Jaeyoon Yoo and Heonseok Ha and Jihun Yi and Jongha Ryu and Chanju Kim and Jung-Woo Ha and Young-Han Kim and Sungroh Yoon},
journal= {arXiv preprint arXiv:1706.09200},
year = {2017}
}