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- W3204564112 abstract "Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc. However, most of the algorithms use flat feature vectors to represent context whereas, in the real world, there is a varying number of objects and relations among them to model in the context. For example, in a music recommendation system, the user context contains what music they listen to, which artists create this music, the artist albums, etc. Adding richer relational context representations also introduces a much larger context space making exploration-exploitation harder. To improve the efficiency of exploration-exploitation knowledge about the context can be infused to guide the exploration-exploitation strategy. Relational context representations allow a natural way for humans to specify knowledge owing to their descriptive nature. We propose an adaptation of Knowledge Infused Policy Gradients to the Contextual Bandit setting and a novel Knowledge Infused Policy Gradients Upper Confidence Bound algorithm and perform an experimental analysis of a simulated music recommendation dataset and various real-life datasets where expert knowledge can drastically reduce the total regret and where it cannot." @default.
- W3204564112 created "2021-10-11" @default.
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- W3204564112 date "2021-01-01" @default.
- W3204564112 modified "2023-09-23" @default.
- W3204564112 title "Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits" @default.
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- W3204564112 doi "https://doi.org/10.1007/978-3-030-86486-6_3" @default.
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