视频推荐与多门混合专家软演员评论家

Dingcheng Li, Xu Li, Jun Wang, P. Li
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引用次数: 20

摘要

本文提出了一种基于强化学习的大规模多目标排名系统,用于优化工业视频分享平台上的短视频推荐。用户反馈中的多重竞争排名目标和隐式选择偏差是现实平台中的主要挑战。为了解决这些挑战,我们将专家和软演员评论家的多门混合集成到排名系统中。我们证明,与仅基于单一策略的系统相比,我们提出的框架可以大大减少损失函数。
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Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic
In this paper, we propose a reinforcement learning based large scale multi-objective ranking system for optimizing short-video recommendation on an industrial video sharing platform. Multiple competing ranking objective and implicit selection bias in user feedback are the main challenges in real-world platform. In order to address those challenges, we integrate multi-gate mixture of experts and soft actor critic into the ranking system. We demonstrated that our proposed framework can greatly reduce the loss function compared with systems only based on single strategies.
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