Sparsity is one of the challenges in recommendation technologies. Traditional collaborative filtering usually evaluates user similarity based on intersection of users' rating items, and it can not acquire accurate recommendation results when user rating data are extremely sparse. In order to eliminate the limitation above, a novel collaborative filtering algorithm based on domain ontology is presented: the method calculates similarity between items according to domain ontology, fills user rating matrix, and calculates users' similarity with adjusted cosine measure. The experiment result shows that it can effectively improve recommendation quality even with extreme sparsity of user rating data.
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