Abstract: Collaborative filtering recommends and predicts the target user’s preferences by using his neighbor user’s preference.The calculation of similarity is the key.Traditional similarity calculation ignores the affection from the co-rated item number rated by common users,and their average similarity rating.That causes poor similarity description among users in case of data sparse.In this paper,we proposed two factors to improve the traditional similarity calculation.Meanwhile,the collaborative filtering algorithm was improved with the improved similarity and it is applied to film recommendation.Simulation results show that the improved collaborative filtering algorithm based on the improved simi-larity can get a lower MAE value than the traditional method,which is helpful to improve the quality of movie recommendation.

Key words: Collaborative filtering,Pearson similarity,Co-rated item,Movie recommendation