Collaborative filtering is a technique to predict users' interests for items by exploiting the behavior patterns of a group of users with similar preferences. This technique has been widely used for recommender systems and has a number of successful applications in E-commerce. In practice, a major challenge when applying collaborative filtering is that a typical user provides ratings for just a small number of items, thus the amount of training data is sparse with respect to the size of the domain. In this paper, we present a method to address this problem. Our method formulates the collaborative filtering problem in a multi-task learning framework by treating each user rating prediction as a classification problem and solving multiple classification problems together. By doing this, the method allows sharing information among different classifiers and thus reduces the effect of data sparsity.
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