  With the recent advancements in information technology there has been a huge
surge in amount of data available. But information retrieval technology has not
been able to keep up with this pace of information generation resulting in over
spending of time for retrieving relevant information. Even though systems exist
for assisting users to search a database along with filtering and recommending
relevant information, but recommendation system which uses content of documents
for recommendation still have a long way to mature. Here we present a Deep
Learning based supervised approach to recommend similar documents based on the
similarity of content. We combine the C-DSSM model with Word2Vec distributed
representations of words to create a novel model to classify a document pair as
relevant/irrelavant by assigning a score to it. Using our model retrieval of
documents can be done in O(1) time and the memory complexity is O(n), where n
is number of documents.
