Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering (CF)-based recommender systems. The performance of matrix MF methods depends on how the system is modeled to mitigate the data sparsity and over-fitting problems. In this paper we aim at improving the performance of MF-based methods through employing imputed ratings of unknown entries. A novel algorithm is proposed based on the classic Multiplicative update rules (MULT), which utilizes imputed ratings to overcome the sparsity problem. Experimental results on three real-world datasets including MovieLens, Jester, and EachMovie reveal the effectiveness of the proposed strategy over state of the art methods. The proposed method is more tolerant against the sparsity of the datasets as compared to other methods including Alternating Least Squares (ALS), Stochastic Gradient Descent (SGD), Regularized Stochastic Gradient Descent (RSGD), Singular Value Decomposition Plus Plus (SVD++) and MULT methods.
