IRJun 28, 2017
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation LearningJaeyoon Yoo, Heonseok Ha, Jihun Yi et al.
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.