Survey of Recommendation Based on Collaborative Filtering

Abstract

Abstract This paper introduces the domestic and international research of collaborative filtering, and discusses the main problems of collaborative filtering algorithm, including data sparsity, cold start and accuracy of similarity measure.Then, future research and development trends of integrating deep learning to recommender systems are pointed out. In order to solve the data sparsity and cold start problems in the personalized recommendation system, a hybrid collaborative filtering recommendation algorithm is proposed, which combines the KNN model and XGBoost model. When deep learning is applied to recommendation system by integrating massive multi-sources heterogeneous data,it could improve the performance of the recommendation system.