Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation
By Lijing Qin, Shouyuan Chen, Xiaoyan Zhu. 2014
208 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Xiangnan He
- Alekh Agarwal
- Tat-seng Chua
- Akshay Krishnamurthy
- Miroslav Dud'ik
- Adith Swaminathan
- J. Langford
- Nathan Kallus
In Information Systems
- Xiangnan He
- Tat-seng Chua
- Wenqiang Lei
- Xiang Wang
- Maarten de Rijke
- Adith Swaminathan
- Chongming Gao
- Miroslav Dud'ik
In Management Science and Operations Research
- Adith Swaminathan
- Alekh Agarwal
- Miroslav Dud'ik
- Akshay Krishnamurthy
- Nathan Kallus
- J. Langford
- Thorsten Joachims
- Xiangnan He
Citations in the corpus
- Off-policy evaluation for slate recommendation (2016, background): “, linear) models relating the metrics to the features describing a slate [2, 31, 15, 10, 29]; this can lead to bias if the model is inaccurate (e.”
- Advances and Challenges in Conversational Recommender Systems: A Survey (2021, background): “, the recommendations should be diverse instead of being limited by similar items (Qin et al., 2014; Liu et al., 2020bb; Ding et al., 2020).”
- Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms (2014, background): “Qin et al. (2014) extends CMAB to contextual bandits and apply it to diversified online recommendations.”