Abstract: In order to reduce the noisy data and data sparsity problems in the collaborative filtering algorithm, and improve the accuracy of the algorithm, a collaborative filtering algorithm based on information entropy and improved similarity is proposed. The user information entropy model is used to judge the noise data to eliminate the interference of the noise data on the experimental results; the improved similarity calculation method for sparse data is used, and all the score data are used. Rather than relying on common scoring items to calculate, it is of great help to alleviate the impact of sparse data on the accuracy of the recommended results. Experimental results show that the algorithm can eliminate the influence of noisy data on the results to a certain extent, alleviate the interference of data sparseness on the accuracy of recommendation results, improve the accuracy of the recommendation algorithm, and alleviate some common problems in traditional recommendation system algorithms. Compared with the traditional collaborative filtering algorithms, the accuracy of the algorithm is higher.

Key words: collaborative filtering algorithm, information entropy, similarity