Abstract: We combined the traditional two memory-based collaborative-filtering methods and proposed a data-based personalized mixed recommendation method for GitHub projects. The method could not only calculate the similar users dynamically to ensure the personalized recommendation, but also obtain the recommendation quality comparable to the item-based method with only small scale of similar users. At the same time, the method solved the data sparsity and cold boot problems of the original method in the face of GitHub, a data set of users and projects of an order of magnitude but with low degree of crossover to some extent by establishing inverse table and using K-means classification. By comparing with the
traditional method, we verified the effectiveness and superiority of the proposed method.

Key words: data analysis, recommendation system, collaborative-filtering technology, cold boot