[Paper Review] Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning

Jaeyoon Yoo, Heonseok Ha+6 more|arXiv (Cornell University)|Jun 28, 2017

Generative Adversarial Networks and Image Synthesis28 references8 citations

TL;DR

This paper proposes Energy-Based Sequence GANs (EB-SeqGANs) for sequential recommendation by combining energy-based GANs with sequence modeling to generate user-preferred item sequences. It establishes a theoretical link between EB-SeqGANs and maximum-entropy imitation learning, enabling end-to-end training that reduces cascading errors and improves recommendation sequence quality compared to behavior cloning approaches.

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.

Motivation & Objective

• To address the challenge of generating coherent, user-specific item sequences in recommender systems.
• To reduce cascading errors common in behavior cloning approaches that train on individual recommendations independently.
• To develop a generative model that learns the full trajectory of user preferences using sequential data.
• To establish a theoretical connection between energy-based GANs and maximum-entropy imitation learning for improved training stability and interpretability.

Proposed method

• Combines energy-based GANs (EBGANs) with sequence GANs (SeqGANs) to model sequential user preferences.
• Uses a generator network to produce next-item predictions based on historical user item sequences.
• Employs a discriminator to distinguish between real user preference sequences and generated ones.
• Reframes the energy function as a feature function, enabling interpretation within the maximum-entropy imitation learning framework.
• Optimizes the generator using a policy gradient approach that backpropagates through the generator's sampling process.
• Treats the sampling distribution in imitation learning as an optimization variable, improving training stability.

Experimental results

Research questions

• RQ1 Can energy-based GANs be effectively adapted to model sequential user preferences in recommendation systems?
• RQ2 How does the proposed EB-SeqGAN framework relate to maximum-entropy imitation learning?
• RQ3 Does the proposed method reduce cascading errors compared to behavior cloning in sequential recommendation?
• RQ4 Can the theoretical connection between EB-SeqGANs and imitation learning lead to more stable and accurate training?
• RQ5 What performance improvements does EB-SeqGAN offer over existing deep learning-based recommendation models?

Key findings

• EB-SeqGANs are theoretically equivalent to maximum-entropy imitation learning when the energy function is interpreted as a feature function.
• The method reduces cascading errors by learning entire recommendation trajectories rather than individual actions.
• The framework enables end-to-end training of sequential generators with improved stability through the energy-based formulation.
• The approach is applicable beyond music recommendation, extending to any sequential recommendation task such as click-stream analysis.
• The method avoids numerical instabilities common in direct imitation learning objective approximation by using gradient-based optimization.
• Empirical results suggest improved sequence-level recommendation quality, though specific metrics are not quantified in the provided text.