DocumentCode
2225064
Title
Notice of Retraction
The Research of E-Commerce Recommendation System Based on Collaborative Filtering
Author
Song Ren-jie ; Liang Ying ; Zhang Xi-hai
Author_Institution
Coll. of Inf. Eng., Northeast DianLi Univ., Jilin, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
3136
Lastpage
3138
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 fully studied in the Collaborative Filtering Recommendation Algorithm and k-means clustering algorithm, on this basis, Proposed the establishment of recommendation system based on the collaborative Filtering Recommendation Algorithm of k-means clustering algorithm. This can significantly reduce the seeking time of algorithm to find the nearest neighbor time, so as to raise the speed of the E-commerce recommendation system.
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 fully studied in the Collaborative Filtering Recommendation Algorithm and k-means clustering algorithm, on this basis, Proposed the establishment of recommendation system based on the collaborative Filtering Recommendation Algorithm of k-means clustering algorithm. This can significantly reduce the seeking time of algorithm to find the nearest neighbor time, so as to raise the speed of the E-commerce recommendation system.
Keywords
electronic commerce; information filtering; recommender systems; E-commerce recommendation system; collaborative filtering recommendation algorithm; k-means clustering algorithm; seeking time reduction; Business; Clustering algorithms; Educational institutions; Filtering algorithms; Filtering theory; Information filtering; Information filters; Information science; International collaboration; Nearest neighbor searches;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.1269
Filename
5455217
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