Research on Commodity Mixed Recommendation Algorithm

International Journal of Advanced Network, Monitoring, and Controls

article

Hao Chang¹  Shengquan Yang¹ 

[1] School of Computer Science and Engineering Xi'an Technological University Xi'an

关键词: E-Commerce;    Recommendation Algorithm;    Decision Tree;    Collaborative Filtering;   

DOI  :  10.21307/ijanmc-2020-021

学科分类：社会科学、人文和艺术（综合）

来源: Asociación Regional De Diálisis Y Trasplantes Renales

【 摘 要 】

With the advent of the era of big data, our lives generate huge amounts of data every day, and the field of e-commerce is no exception. It is particularly important to analyze these data and recommend products. It is reported that through the recommendation algorithm, Amazon has increased its sales by about 30%. Among the recommended algorithms, the collaborative filtering algorithm is currently relatively mature and has achieved very good results in various fields. But the traditional collaborative filtering algorithm is too rough when calculating the similarity and prediction score, and the efficiency is very low. We combine the traditional collaborative filtering algorithm with the decision tree algorithm, and improve the traditional recommendation algorithm, create a collaborative filtering decision tree algorithm to recommend products, and run the new collaborative filtering decision tree algorithm on the Hadoop platform on. Experiments show that the improved algorithm makes the accuracy of recommendation significantly improved.

【 授权许可】

CC BY-NC-ND