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Scalable collaborative filtering using incremental update and local link prediction

The traditional collaborative filtering approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. To address these problems, we present a novel scalable item-based collaborative filtering method by using incremental update and local link prediction. By subdividing the computations and analyzing the factors in different cases of item-to-item similarity, we design the incremental update strategies in item-based CF, which can make the recommender system more efficient and scalable. Based on the transitive structure of item similarity graph, we use the local link prediction method to find implicit candidates to alleviate the lack of neighbors in predictions and recommendations caused by the sparsity of data. The experiment results validate that our algorithm can improve the performance of traditional CF, and can increase the efficiency in recommendations.