Abstract:Aiming at the poor recommendation quality due to the data sparsity problem of traditional collaborative filtering recommendation, this paper puts forward an improved collaborative filtering algorithm.The improved algorithm proposes a collaborative filtering algorithm based on the similarity integration of item categories and user interests to make optimization on the similarity calculation.The algorithm does not simply concentrate on similarity calculation, but divides it into two aspects:users-item category interest similarity and users-item category rating similarity, which will finally be integrated with appropriate weights to get the final similarity.After a series of verification and comparison carried out on the MovieLens public data set, it is concluded that the improved algorithm based on data sparsity of collaborative filtering indeed plays a positive role in reducing the influence caused by data sparsity and improves the accuracy of recommendation.