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In this paper we present an approach that tries to alleviate the main item-based collaborative filtering (CF) drawback - the sparsity and the first-rater problem. The contents of items are combined into the item-based CF to find similar items and combined similarity is used to generate predictions. The first step concentrates in using association rules mining methods to discover new similarity relationships among attributes. The second step is to exploit this similarity during the calculation of similar item. Finally, new similarity and rating similarity measures are combined 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.