Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cluster Based Deep Contextual Reinforcement Learning for top-k
  Recommendations   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.

31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin