Uploaded January 2022 | Updated September 2026, 3 hours ago
ArtIAMAS Seminar Series: Top-K Ranking Deep Contextual Bandits for Information Selection Systems
Jade Freeman
Chief
Battlefield Information Systems Branch
Army Research Laboratory
In today’s technology environment, information is abundant, dynamic, and heterogeneous in nature. Automated filtering and prioritization of information is based on the distinction between whether the information adds substantial value toward one’s goal or not. Contextual multi-armed bandit has been widely used for learning to filter contents and prioritize according to user interest or relevance. Learn-to-Rank technique optimizes the relevance ranking on items, allowing the contents to be selected accordingly. We propose a novel approach to top-K rankings under the contextual multi-armed bandit framework. We model the stochastic reward function with a neural network to allow non-linear approximation to learn the relationship between rewards and contexts. We demonstrate the approach and evaluate the performance of learning from the experiments using real world data sets in simulated scenarios. Empirical results show that this approach performs well under the complexity of a reward structure and high dimensional contextual features.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
ArtIAMAS Seminar Series: Top-K Ranking Deep Contextual Bandits for Information Selection Systems
Jade Freeman
Chief
Battlefield Information Systems Branch
Army Research Laboratory
In today’s technology environment, information is abundant, dynamic, and heterogeneous in nature. Automated filtering and prioritization of information is based on the distinction between whether the information adds substantial value toward one’s goal or not. Contextual multi-armed bandit has been widely used for learning to filter contents and prioritize according to user interest or relevance. Learn-to-Rank technique optimizes the relevance ranking on items, allowing the contents to be selected accordingly. We propose a novel approach to top-K rankings under the contextual multi-armed bandit framework. We model the stochastic reward function with a neural network to allow non-linear approximation to learn the relationship between rewards and contexts. We demonstrate the approach and evaluate the performance of learning from the experiments using real world data sets in simulated scenarios. Empirical results show that this approach performs well under the complexity of a reward structure and high dimensional contextual features.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
