Content Based Document Recommender using Deep Learning
This addresses the inefficiency in information retrieval for users dealing with large data volumes, though it appears incremental as it builds on existing models like C-DSSM and Word2Vec.
The paper tackles the problem of content-based document recommendation by proposing a deep learning model that combines C-DSSM with Word2Vec to classify document pairs as relevant or irrelevant, achieving retrieval in O(1) time and memory complexity of O(n).
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.