Finding item neighbors in item-based collaborative filtering by adding item content

In this paper we present an approach to that tries to alleviate the main item-based collaborative filtering (CF) drawback - the sparsity and the first-rater problem. By combining the contents of items into the item-based CF to find similar items and use the combined similarity to generate predictions. The first step concentrates is using association rules mining methods to discover new similarity relationships among attributes. The second step is to exploit this similarity during the calculation of item similar. Finally, combines new similarity and rating similarity measures to find neighbor item in item-based CF algorithm and generating ratings predictions based on a combined similarity measure. The experiments show that this novel approach can achieve better prediction accuracy than traditional item-based CF algorithm.