Music recommendation system based on matrix factorization technique -SVD

Recommender systems have been proven to be valuable means for web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. With the development of electronic commerce systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Sparsity of users´ ratings is the major reason causing the poor quality. To address this issue, Item based collaborative filtering recommendation algorithm based on singular value decomposition (SVD) is presented. This Paper uses SVD for dimensionality reduction, and then uses Euclidian distance as dissimilarity measure to find the target users´ neighbors, lastly produces the recommendations. The collaborative filtering recommendation algorithm based on SVD can alleviate the sparsity problems of the user item rating dataset, and can provide better recommendation than traditional collaborative filtering algorithms.