Recommender systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given item. The main types of recommender systems namely collaborative filtering and content-based filtering suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage. There has been some work in the literature to increase the scalability by reducing the dimensions of the recommender system dataset using singular value decomposition (SVD); however, due to sparsity it results in inaccurate recommendations. In this paper, we show how a careful selection of an imputation source in singular value decomposition based recommender system can provide potential benefits ranging from cost saving, to performance enhancement. The proposed missing value imputation methods have the ability to exploit any underlying data correlation structures and hence have been proven to exhibit much superior accuracy and performance as compared to the traditional missing value imputation strategy—item average of the user-item rating matrix—that has been the preferred approach in the literature to resolve this problem. By extensive experimental results on three different dataset, we show that the proposed approaches outperform traditional one and moreover, they provide better recommendation under new user cold-start problem, new item cold-start problem, long tail problem, and sparse conditions.
By: Mustansar Ali Ghazanfar and Adam Prugel-Bennett