Top-K Ranking Deep Contextual Bandits for Information Selection Systems
- URL: http://arxiv.org/abs/2201.13287v1
- Date: Fri, 28 Jan 2022 15:10:44 GMT
- Title: Top-K Ranking Deep Contextual Bandits for Information Selection Systems
- Authors: Jade Freeman and Michael Rawson
- Abstract summary: We propose a novel approach to top-K rankings under the contextual multi-armed bandit framework.
We model the reward function with a neural network to allow non-linear approximation to learn the relationship between rewards and contexts.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 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.
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