Personalized Recommendation via Parameter-Free Contextual Bandits
By Liang Tang, Yexi Jiang, Lei Li, Chunqiu Zeng, Tao Li. 2015
95 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
- Susan Athey
- Guido W. Imbens
- Xing Xie
- Chunqiu Zeng
- Shekoofeh Mokhtari
- Tao Li
- Qing Wang
- Maria Dimakopoulou
In Information Systems
- Xing Xie
- Fuzheng Zhang
- Katja Hofmann
- Filip Radlinski
- Chunqiu Zeng
- Zhenhui Jessie Li
- Shekoofeh Mokhtari
- Nicholas Jing Yuan
In Management Science and Operations Research
- Susan Athey
- Guido W. Imbens
- Katja Hofmann
- Maria Dimakopoulou
- Xing Xie
- Chunqiu Zeng
- Filip Radlinski
- Zhenhui Jessie Li
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.”
- Estimation Considerations in Contextual Bandits (2017, methodology): “The method of bootstrap for contextual bandits is also discussed in [35] and GLM are discussed in [28].”
- Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit (2016, methodology): “A most recent research work on the contextual bandit problem in [25] comes up with a novel parameter-free algorithm based on a principled sampling approach.”
- Towards Conversational Recommender Systems (2016, background): “Most work in this area relies on the availability of user/item features and assumes that the reward of an arm for a given context is a linear/logistic function of the concatenated feature of arm-context [16, 31].”