DocumentCode
1649795
Title
Notice of Retraction
Research on recommender system in e-commerce based on Web mining
Author
Shen Zihao ; Wang Hui
Author_Institution
Coll. of Comput., Henan Polytech. Univer, Jiaozuo, China
Volume
2
fYear
2010
Firstpage
360
Lastpage
363
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper introduces Web mining technologies, analyzes the Web mining process often used in recommender system. We propose a recommender system model framework and design the recommender system in E-commerce. The model framework consists of data acquisition module, off-line module and online module, which solves the balance problem between recommendation accuracy and respond efficiency. The experiment data shows that the recommender system effectively improves the accuracy of the algorithm, reduces the response time, and has a very good recommendation effect and a certain application value.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper introduces Web mining technologies, analyzes the Web mining process often used in recommender system. We propose a recommender system model framework and design the recommender system in E-commerce. The model framework consists of data acquisition module, off-line module and online module, which solves the balance problem between recommendation accuracy and respond efficiency. The experiment data shows that the recommender system effectively improves the accuracy of the algorithm, reduces the response time, and has a very good recommendation effect and a certain application value.
Keywords
Internet; data acquisition; data mining; electronic commerce; recommender systems; Web mining; data acquisition module; e-commerce; recommendation effect; recommender system; Accuracy; Analytical models; Engines; Gold; Recommender systems; E-commerce; Web mining; association analysis; data preprocessing; recommender system;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Management Science (ICAMS), 2010 IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6931-4
Type
conf
DOI
10.1109/ICAMS.2010.5552941
Filename
5552941
Link To Document