Privacy Preservation of Product Usage Experience of Customer by using Matrix Factorization using Techniques

The increasing ability to track and collect large amounts of data with the use of current hardware technology has lead to an interest in the development of data mining algorithms which preserve user privacy. A recently proposed technique addresses the issue of privacy preservation by perturbing the data and reconstructing distributions at an aggregate level in order to perform the mining. This method is able to retain privacy while accessing the information implicit in the original attributes. Some of the most successful realizations of latent factor models are based on matrix factorization. In its basic form, matrix factorization characterizes both items and users by vectors of factors inferred from item rating patterns. High correspondence between item and user factors leads to a recommendation. These methods have become popular in recent years by combining good scalability with predictive accuracy. In addition, they offer much flexibility for modeling various real-life situations. One strength of matrix factorization is that it allows incorporation of additional information. When explicit feedback is not available, recommender systems can infer user preferences using implicit feedback, which indirectly reflects opinion by observing user behavior including purchase history, browsing history, search patterns, or even mouse movements. Implicit feedback usually denotes the presence or absence of an event, so it is typically represented by a densely filled matrix.