Movie recommender system using item based collaborative filtering technique

Recommender systems being a part of information filtering system are used to forecast the bias or ratings the user tend to give for an item. Among different kinds of recommendation approaches, collaborative filtering technique has a very high popularity because of their effectiveness. These traditional collaborative filtering systems can even work very effectively and can produce standard recommendations, even for wide ranging problems. For item based on their neighbor's preferences Collaborative filtering techniques creates better suggestions than others. Whereas other techniques like content based suffers from poor accuracy, scalability, data sparsity and big-error prediction. To find these possibilities we have used item-based collaborative filtering approach. In this Item based collaborative filtering technique we first examine the User item rating matrix and we identify the relationships among various items, and then we use these relationships in order to compute the recommendations for the user.