In this section we study a class of item-based recommendation algorithms for producing predictions to users. Unlike the user-based collaborative filtering algorithm discussed in Section 2 the item-based approach looks into the set of items the target user has rated and computes how similar they are to the target item i and then selects k most similar items . At the same time their corresponding similarities are also computed. Once the most similar items are found, the prediction is then computed by taking a weighted average of the target user's ratings on these similar items. We describe these two aspects namely, the similarity computation and the prediction generation in details here.