An effective distributed predictive model with Matrix factorization and random forest for Big Data recommendation systems
作者：
Highlights：
• A distributed predictive model is proposed for Big Data recommendations.
• A data partitioning strategy is adopted to speed up Big Data processing.
• A novel learning process is devised to accelerate parallel and distributed training.
• Matrix factorization and Random forest are used to improve the prediction accuracy.
• Our proposal is highly competitive compared with state-of-the-art approaches.
摘要
•A distributed predictive model is proposed for Big Data recommendations.•A data partitioning strategy is adopted to speed up Big Data processing.•A novel learning process is devised to accelerate parallel and distributed training.•Matrix factorization and Random forest are used to improve the prediction accuracy.•Our proposal is highly competitive compared with state-of-the-art approaches.
论文关键词：Big Data,Distributed computing,Random forest,Matrix factorization,Apache spark,Recommendation systems
论文评审过程：Received 5 December 2018, Revised 22 June 2019, Accepted 22 June 2019, Available online 22 June 2019, Version of Record 8 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.046
