Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

@article{Ji2021ReinforcementLT,
  title={Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation},
  author={Luo Ji and Qin Qi and Bingqing Han and Hongxia Yang},
  journal={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  year={2021}
}
  • Luo Ji, Qin Qi, Hongxia Yang
  • Published 20 August 2021
  • Computer Science
  • Proceedings of the 30th ACM International Conference on Information & Knowledge Management
Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challenging problem. In order to alleviate the cold-start issue, most existing methods introduce content and contextual information as the auxiliary information. Nevertheless, these methods assume the recommended items behave steadily over time, while in a typical E-commerce scenario, items generally have very different… 

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