A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System

By Djallel Bouneffouf, Amel Bouzeghoub, Alda Lopes Gançarski. 2012

150 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

1. Xing Xie
2. Fuzheng Zhang
3. Liang Tang
4. Xiaoyan Zhu
5. Qingyun Wu
6. Chunqiu Zeng
7. Yexi Jiang
8. Shouyuan Chen

In Information Systems

1. Xing Xie
2. Fuzheng Zhang
3. Tao Li
4. Shekoofeh Mokhtari
5. Qing Wang
6. Chunqiu Zeng
7. Liang Tang
8. Xiaoyan Zhu

In Management Science and Operations Research

1. Chunqiu Zeng
2. Shekoofeh Mokhtari
3. Tao Li
4. Qing Wang
5. Xing Xie
6. Liang Tang
7. Xiaoyan Zhu
8. Qingyun Wu

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “Agroup ofwork [5, 7, 23, 40, 44, 50] begin to formulate the problem as a Contextual Multi-Armed Bandit (MAB) problem, where the context contains user and item features.”
• Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function) (2018, background): “A similar rule is the contextual- greedy reinforcement learning policy proposed in [11]; both of these decision rules combine explore/exploit tradeoffs with the notion that certain actions might be too costly or dangerous to take as experiments in certain situations.”
• Recommender Systems (2016, unknown): “Bandit algorithms for recommender systems are discussed in [92, 348].”
• Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation (2014, background): “To recommend items to users without sufficient historical records, several studies formulate this task as a multi-armed bandit problem [14, 15, 5].”
• Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit (2016, methodology): “Both LinUCB and LogUCB algorithms extend the UCB algorithm to contextual bandits [5, 19].”
• Learning Hidden Features for Contextual Bandits (2016, background): “Contextual bandits are especially advantageous when the space of recommendation is large but the payoffs are interrelated, such as content recommendation [15, 6, 26] and display advertising [8, 17].”
• Ensemble contextual bandits for personalized recommendation (2014, background): “Existing studies propose different contextual bandit algorithms and show their empirical performances on various real-world data sets [5, 6, 13].”