Singular Value Decomposition in Recommender Systems
Many websites nowadays such as Amazon, Ebay have used different kinds of Recommender Systems to predict ratings of items from their clients, so that they could suggest which items are more likely to be purchased. Nevertheless, problems arise as a data set can be both too large and too sparse to predict ratings. Hence, the size of a data set and its information sufficiency should be taken into consideration in order to make accurate predictions efficiently. In this project, we examine a method of matrix factorization called Singular Value Decomposition to approach the aforementioned problems by applying the method to real data that we retrieve from Grouplens.