Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems

By Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi. 2017

88 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Xing Xie

In Information Systems

1. Xing Xie
2. Fuzheng Zhang
3. Xiangyu Zhao
4. Jiliang Tang
5. Dawei Yin
6. Long Xia
7. Zhuoye Ding
8. Zhenhui Jessie Li

In Management Science and Operations Research

1. Xiangyu Zhao
2. Jiliang Tang
3. Xing Xie
4. Zhenhui Jessie Li
5. Nicholas Jing Yuan
6. Fuzheng Zhang
7. Dawei Yin
8. Yang Xiang

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, background): “In addition, different from [48], our model can estimate user activeness at any time (not just when user returns).”
• Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018, background): “Note that since user’s preference is relatively stable during a short time slot [30], we assume that user will give same feedback to a C at state + , s − ) .”