Matrix Factorization Methods for Recommender Systems
Shameem Ahamed Puthiya Parambath · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2013
This thesis is a comprehensive study of matrix factorization methods used in recommender systems. We study and analyze the existing models, specifically probabilistic models used in conjunction with matrix factorization methods, for recommender systems from a machine learning perspective. We implement two different methods suggested in scientific literature and conduct experiments on the prediction accuracy of the models on the Yahoo! Movies rating dataset.