Recommendation System Using Item Based And User Based Collaboration Filtering
Recommender systems attempt to highlight items that a target user is likely to find interesting. Recommender
systems apply knowledge discovery techniques to the problem of making personalized recommendations for information,
products or services during a live interaction. Collaborative filtering, the most successful recommender system technology to
date, helps people make choices based on the opinions of other people. Existing collaborative filtering methods, mainly user-
based and item-based methods, predict new ratings by aggregating rating information from either similar users or items.
Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding
unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated
with a user, what queries (new ratings) would most improve the quality of the recommendations made? Recommender
systems apply data analysis techniques to the problem of helping users find the items they would like to purchase at E-
Commerce sites by producing a predicted likeliness score or a list of top-N recommended items for a given user. We apply
Clustering algorithms for finding nearest similar item. To finding nearest item for this we use C++ language. We apply
improved K-mean algorithms method on preprocessed data. Finally we proposed a method that can increase accuracy as
respect to user based system.
Keywords: Recommender Systems, Collaborative Filtering Recommendation algorithm-mean algorithm.