A Recommendation System Based on Extreme Gradient Boosting Classifier

Shopping online has become the mainstream way of shopping of the society in recent years. Consumer behavior records on shopping website contain a lot of important information that can be used as basis for commodity recommendations. But traditional collaborative filtering-based recommendation systems are sometimes difficult to handle noise in behavior records. In this paper, we proposed a complex model based on eXtreme Gradient Boosting(xgboost) algorithm with a series of methods of features extraction to build a recommender system based on behavior records of consumers got from Alibaba mobile client. The system had a good performance on the data set with f1-score of 7.97% and has high time efficiency.