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arXiv · 1706.09200

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

Jaeyoon Yoo·Heonseok Ha·Jihun Yi·Jongha Ryu·Chanju Kim·Jung-Woo Ha·Young-Han Kim·Sungroh Yoon
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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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Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, Sungroh Yoon. 2017-06-28. Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning. https://arxiv.org/abs/1706.09200

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