• Corpus ID: 239049409

Locality-Sensitive Experience Replay for Online Recommendation

@article{Chen2021LocalitySensitiveER,
  title={Locality-Sensitive Experience Replay for Online Recommendation},
  author={Xiaocong Chen and Lina Yao and Xianzhi Wang and Julian McAuley},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10850}
}
Online recommendation requires handling rapidly changing user preferences. Deep reinforcement learning (DRL) is an effective means of capturing users’ dynamic interest during interactions with recommender systems. Generally, it is challenging to train a DRL agent, due to large state space (e.g., user-item rating matrix and user profiles), action space (e.g., candidate items), and sparse rewards. Existing studies leverage experience replay (ER) to let an agent learn from past experience. However… 

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