Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective

@article{Zhao2020MaximizingCU,
  title={Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective},
  author={Yifei Zhao and Yu-Hang Zhou and Mingdong Ou and Huan Xu and Nan Li},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Yifei ZhaoYu-Hang Zhou Nan Li
  • Published 2 June 2020
  • Computer Science
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging user browsing (i.e., more items exposured). Existing works often study these two tasks separately, thus tend to result in sub-optimal results. In this paper, we study this problem from an online optimization perspective, and propose a… 
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