A Comprehensive Recommender System Model: Improving Accuracy for Both Warm and Cold Start Users

Abstract

Sparsity of the ratings available in the recommender system database makes the task of rating prediction a highly underdetermined problem. This poses a limit on the accuracy and the quality of prediction. In this paper, we utilize secondary information pertaining to user’s demography and item categories to enhance prediction accuracy. Within the matrix factorization framework, we introduce additional supervised label consistency terms that match the user and item factor matrices to the available secondary information (metadata). Matrix factorization model—conventionally employed in collaborative filtering techniques—yields dense user and dense item factor matrices—the assumption is that users have an affinity toward all latent factors and items possess all latent factors. Our formulation, based on a recent work, aims to recover a dense user and a sparse item factor matrix—this is a more reasonable model. Human beings show a natural interest toward all the factors, but every item cannot possess all the factors; this leads to a sparse item factor matrix. A natural outcome of our proposal is a solution to the pure cold start problem. We utilize the label consistency map generated from the proposed model to make reasonable recommendations for new users and new items which have not (been) rated yet. We demonstrate the performance of our model for a movie recommendation system. We also design an efficient algorithm for our formulation.