DocumentCode
3348172
Title
Notice of Retraction
Based on association rules of personalized teaching resources recommendation model
Author
Zhi-Yu Chen ; Xin Liu ; Ming Hu
Author_Institution
Sch. of Comput. Sci. & Eng., Changchun Univ. of Technol., Changchun, China
Volume
4
fYear
2011
fDate
26-28 July 2011
Firstpage
2015
Lastpage
2018
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.
Aimed at the network teaching system to provide recommendation services for learners that there are some shortage. By analyzing the characteristics of personalized teaching, it designed a personalized teaching resources recommendation model which based on association rules. Teaching resources in this paper are organized by the relationship of curriculum knowledge points, the learners are clustered by the similar characteristics, thus to obtain learners - knowledge points two-dimensional table. This paper presents an improved Apriori algorithm. Association rules are obtained through looking up frequent item sets in the two-dimensional table, to recommend the knowledge, thus to achieve the objective of personalized recommendation.
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.
Aimed at the network teaching system to provide recommendation services for learners that there are some shortage. By analyzing the characteristics of personalized teaching, it designed a personalized teaching resources recommendation model which based on association rules. Teaching resources in this paper are organized by the relationship of curriculum knowledge points, the learners are clustered by the similar characteristics, thus to obtain learners - knowledge points two-dimensional table. This paper presents an improved Apriori algorithm. Association rules are obtained through looking up frequent item sets in the two-dimensional table, to recommend the knowledge, thus to achieve the objective of personalized recommendation.
Keywords
data mining; knowledge engineering; recommender systems; teaching; apriori algorithm; association rule; curriculum knowledge point; knowledge recommendation service; learners knowledge points two dimensional table; network teaching system; personalized teaching resource recommendation model; Algorithm design and analysis; Association rules; Databases; Educational institutions; Partitioning algorithms; association rules; cluster; knowledge points; personalized;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022411
Filename
6022411
Link To Document