融合协同过滤的XGBoost推荐算法

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 uses coarse grain to recall according to the collaborative filtering recommendation algorithm based on user similarity, and get a recall set of some users. Then using XGBoost algorithm to predict the items in the recall set. Secondly, for users with cold-start problems, it can directly use XGBoost algorithm to predict the items in the candidate set. Finally, the algorithm uses the online evaluation data set of CCIR2018 personalized recommendation evaluation, and puts the recommendation results on the online platform provided by Zhihu for evaluation. The evaluation results show that the algorithm in this paper can solve the cold-start problem of users with high efficiency and accuracy. It has achieved remarkable recommendation effect in the online evaluation platform and gets the third prize.