Collaborative Filtering Based On Transitive Correlations Between Items

With existing collaborative filtering algorithms, a user has to rate a sufficient number of items, before receiving reliable recommendations. To overcome this limitation, we provide the insight that correlations between items can form a network, in which we examine transitive correlations between items. The emergence of power laws in such networks signifies the existence of items with substantially more transitive correlations. The proposed algorithm finds highly correlative items and provides effective recommendations by adapting to user preferences. We also develop pruning criteria that reduce computation time. Detailed experimental results illustrate the superiority of the proposed method.