Collaborative Filtering Algorithm Based on Preference of Item Properties

To address the shortcomings of traditional collaborative filtering algorithm for data sparsity of the user–item rating matrix, a collaborative filtering algorithm based on the preferences of the item properties is proposed. The algorithm calculates the similarity between users through user preference value for item properties. Then, it predicts item ratings that users have not rated based on user similarity to increase data density of the original user–item rating matrix. Finally, it adopts the corresponding collaborative filtering algorithm based on the item properties preference to achieve the personalized recommendation. The experimental results show that this method can effectively improve the quality of the recommendations.