Boosting a Hybrid Model Recommendation System for Sparse Data using Collaborative Filtering and Deep Learning

The exponential increase in the volume of online data has generated a confront of overburden of data for online users, which slow down the suitable access to products of pursuit on the Web. This contributed to the need for recommendation systems. Recommender system is a special form of intelligent technique that takes advantage of past user transactions on products to give recommendations of products. Collaborative filtering has turn out to be the commonly adopted method of providing users with customized services, except that it endures the problem of sparsely rated inputs. For collaborative filtering, we introduce a deep learning-based architecture which evaluates a discrete factorisation of vectors from sparse inputs. The characteristics of the products are retrieved using a deep learning model, denoising auto encoders. The traditional collaborative filtering algorithm that predicts and uses the past history of consumer interest and product characteristics are updated with the characteristics obtained by deep learning model for sparse rated inputs. The results of sparse data problem tested on MovieLens data set will greatly enhance the user and product transaction.