Energy-based sequence gans for recommendation and their connection to imitation learning
arXiv preprint arXiv:1706.09200, 2017•arxiv.org
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.
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.
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.
arxiv.org