Abstract: Collaborative filtering algorithm is one of the most successful and useful technologies in recommendation systems.Cosine similarity and Pearson correlation coefficient are two of the most widely used traditional algorithms to calculate the similarity in collaborative filtering algorithm.In order to reduce the error,an improved collaborative filtering recommendation algorithm was proposed in view of the disadvantages of the two traditional similarity algorithms.The two traditional algorithms were improved by importing two parameters,one of them was proposed for considering the rating habits of users,and the other was imported to measure the difference of items chosen by users.User’s preference is related to project attributes,therefore,a parameter was designed to measure it.The new algorithm was constructed by the improved traditional algorithm and user’s preference for attributes.The results of experiment on MovieLens dataset show that the proposed algorithm has lower mean absolute error (MAE) and root mean square error (RMSE),and has better performance by using the two parameters compared with traditionalalgorithms.

Key words: Collaborative filtering, Parameter for adjustment, Preference for attributes, Recommendation system, User similarity