Research on personalized recommendation of distance education resources based on spark
Zhufeng Qiao, Guo Jianxin, Jichun Zhao · 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC) · 2018
In the face of mass distance education resource data processing, the traditional collaborative recommendation algorithm is inefficient in single machine. This paper proposes an improved recommendation strategy based on Spark parallel computing model. This strategy can give full play to the iterative computing advantage of Spark and apply it to the recommendation of distance education resources. The efficiency and recommendation quality of traditional algorithm and improved algorithm under distributed and non distributed conditions are compared. The experimental results show that the personalized recommendation algorithm based on Spark computing model can effectively improve the recommendation quality and recommendation efficiency of distance education resources.