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In the recommendation system, this paper proposed an XGBoost recommendation algorithm to integrate collaborative filtering based on the cold-start problem of users. Firstly, it used coarse grain to recall according to the collaborative filtering recommendation algorithm based on user similarity, and got a recall set of some users. Then it used XGBoost algorithm to predict the items in the recall set. Secondly, for users with cold-start problems, it could directly use XC Boost algorithm to predict the items in the candidate set. Finally, the algorithm used the online evaluation data set of CCIR2018 personalized recommendation evaluation, and put the recommendation results on the online platform provided by Zhihu for evaluation. The evaluation results show that the proposed algorithm can solve the cold-start problem of users with high efficiency and accuracy. It achieves remarkable recommendation effect in the online evaluation platform. [ABSTRACT FROM AUTHOR]