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Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
- Publication Year :
- 2017
-
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.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1706.09200
- Document Type :
- Working Paper