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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.