Contextual Bandits Evolving Over Finite Time

--Contextual bandits have the same exploration-exploitation tradeoff as standard multi-armed bandits. On adding positive externalities that decay with time, this problem becomes much more difficult as wrong decisions at the start are hard to recover from. We explore existing policies in this setting and highlight their biases towards the inherent reward matrix. We propose a rejection based policy that achieves a low regret irrespective of the structure of the reward probability matrix. In the context of restaurant recommendation systems, users can generally be classified into multiple user types with different preferences for different restaurants.