Ensemble contextual bandits for personalized recommendation

By Liang Tang, Yexi Jiang, Lei Li, Tao Li. 2014

119 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
2. Chunqiu Zeng
3. Shekoofeh Mokhtari
4. Tao Li
5. Qing Wang

In Information Systems

1. Xing Xie
2. Fuzheng Zhang
3. Chunqiu Zeng
4. Zhenhui Jessie Li
5. Shekoofeh Mokhtari
6. Nicholas Jing Yuan
7. Tao Li
8. Zihan Zheng

In Management Science and Operations Research

1. Xing Xie
2. Chunqiu Zeng
3. Zhenhui Jessie Li
4. Shekoofeh Mokhtari
5. Nicholas Jing Yuan
6. Tao Li
7. Fuzheng Zhang
8. Qing Wang

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “[39] uses an ensemble of bandits to improve the performance, [40] proposes a parameter-free model, and [50] addresses the time-varying interest of users.”
• Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit (2016, methodology): “In [24], an ensemble strategies combined with a meta learning paradigm is proposed to stabilize the output of contextual bandit algorithms.”