融合协同过滤的XGBoost推荐算法

在推荐系统中，针对用户的冷启动问题，提出一种融合协同过滤的XGBoost推荐算法。根据基于用户相似度的协同过滤推荐算法进行粗粒度召回，得到部分用户的召回集，使用XGBoost算法对召回集中的项目进行预测。对于存在冷启动问题的用户，直接使用XGBoost算法对候选集中的项目进行预测。该算法采用CCIR2018个性化推荐评测的在线评测数据集，并将推荐结果投放到知乎提供的线上平台进行评测。评测结果表明，该算法可以解决用户的冷启动问题，具有很高的执行效率，准确度高，在线上评测中取得显著的推荐效果。

XGBoost recommendation algorithm with collaborative filtering

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 XGBoost 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.