Abstract: The recommender system can help users solve the problem of information overload effectively and has been widely applied in major online shopping platforms.For users,a good recommendation algorithm can help them find products which meet their needs from a large number of products.For merchants,timely presentation of appropriate items to users can help merchants achieve precision marketing,discover long-tail products and recommend them to users to increase sales.Collaborative filtering and content-based recommendation are currently mature recommendation methods,but these methods have problems such as data sparsity,cold start,poor scalability,and difficulty in extracting multimedia information features.Therefore,this paper proposes a personalized recommendation algorithm based on the fusion of LR-GBDT-XGBOOST,which can effectively alleviate the above problems.Experiments are carried out under the official dataset of the Alibaba Tianchi big data competition.The results show that the proposed algorithm reduces the recommended sparsity and improves the accuracy of the recommendation.

Key words: Collaborative filtering, E-commerce, Mixed recommendation, Recommender systems