Solving the Sparsity Problem: Collaborative Filtering via Indirect Similarities

Collaborative filtering is an important technique of information filtering, commonly used to predict the interest of a user for a new item. In collaborative filtering systems, this prediction is made based on user-item preference data involving similar users or items. When the data is sparse, however, direct similarity measures between users or items provide little information that can be used for the prediction. In this paper, we present a new collaborative filtering approach that computes global similarities between pairs of items and users, as the equilibrium point of a system relating user similarities to item similarities. We show how this approach extends the classical techniques based on direct similarity, and illustrate, by testing on various datasets, its advantages over such techniques.