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Item-based Collaborative Filtering Algorithm

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 $\{i_1, i_2, \ldots, i_k\}$. At the same time their corresponding similarities $\{s_{i1}, s_{i2}, \ldots,
s_{ik}\}$ 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.



 

Badrul M. Sarwar
2001-02-19