  Rapid advancements in the E-commerce sector over the last few decades have
led to an imminent need for personalised, efficient and dynamic recommendation
systems. To sufficiently cater to this need, we propose a novel method for
generating top-k recommendations by creating an ensemble of clustering with
reinforcement learning. We have incorporated DB Scan clustering to tackle vast
item space, hence in-creasing the efficiency multi-fold. Moreover, by using
deep contextual reinforcement learning, our proposed work leverages the user
features to its full potential. With partial updates and batch updates, the
model learns user patterns continuously. The Duelling Bandit based exploration
provides robust exploration as compared to the state-of-art strategies due to
its adaptive nature. Detailed experiments conducted on a public dataset verify
our claims about the efficiency of our technique as com-pared to existing
techniques.
