Markov Model Based Item Prediction Using Collaborative Filtering

Collaborative filtering is a technique for reducing information overload and is achieved by predicting the applicability of items to users. In collaboration Filtering we use recommendation system because Recommender system applies knowledge discovery techniques to the problem of making personalized recommendation for information. Products or services during a live interaction. these system especially the k-nearest neighbor collaborative filtering based once, are achieving widespread success on the Web. In clustering algorithms, the applicability is predicted by the weighted sum model of ratings of k nearest items. This paper considers a new approach to user-item clustering in collaborative filtering algorithm using Markov Model for recommender systems. We use metrics like prediction strength, and after getting Matrix we apply Markov model for prediction which gives better prediction than weighted sum.