Jointly Learning to Recommend and Advertise

@article{Zhao2020JointlyLT,
  title={Jointly Learning to Recommend and Advertise},
  author={Xiangyu Zhao and Xudong Zheng and Xiwang Yang and Xiaobing Liu and Jiliang Tang},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Xiangyu Zhao, Xudong Zheng, +2 authors Jiliang Tang
  • Published 28 February 2020
  • Computer Science
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recommending and advertising strategies by different teams separately via different techniques, which may lead to suboptimal overall performances. To this end, in this paper, we propose a novel two-level reinforcement learning framework to jointly optimize the recommending and advertising strategies, where the first level… 
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