  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.
