Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

@article{Zou2019ReinforcementLT,
  title={Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems},
  author={Lixin Zou and Long Xia and Zhuoye Ding and Jiaxing Song and Weidong Liu and Dawei Yin},
  journal={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2019}
}
  • Lixin ZouLong Xia Dawei Yin
  • Published 13 February 2019
  • Computer Science
  • Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Recommender systems play a crucial role in our daily lives. [] Key Method FeedRec includes two components: 1)~a Q-Network which designed in hierarchical LSTM takes charge of modeling complex user behaviors, and 2)~a S-Network, which simulates the environment, assists the Q-Network and voids the instability of convergence in policy learning. Extensive experiments on synthetic data and a real-world large scale data show that FeedRec effectively optimizes the long-term user engagement and outperforms state-of…

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