An Intelligent Personalized Learning Model Based on Community Discovery Method

Min Xu; Yuan Zhang; Mei Qi Fang; Ning Li

doi:10.4028/www.scientific.net/AMR.159.248

Paper Titles

The Application of Data Mining in Distance Instruction
p.228

Image Enhancement Based on Fractional Differential and Image Entropy
p.232

Research and Utilization Status of Natural Bamboo Fiber
p.236

Study on the Pretreatment of the Preparation of Bamboo Fiber and its Bleaching Technology
p.242

An Intelligent Personalized Learning Model Based on Community Discovery Method
p.248

Research on Personalized Learning Service Based on Collaborative Filtering Method
p.252

Research on Harmonic Suppression and Power Compensation of Aluminum Electrolytic Rectifier
p.258

Safety Evaluation of Urban Flood Control System Based on Variable Fuzzy Pattern Recognition
p.264

Refitting Sites Selection of Ocean Trawler Based on Fuzzy Matter-Element Model and Information Entropy Theory
p.270

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 159An Intelligent Personalized Learning Model Based...

An Intelligent Personalized Learning Model Based on Community Discovery Method

Abstract:

In this paper, we proposed a model of support personalized learning based on SGCL (Social Group Collaborative Learning System). In the model, we provide two algorithms to discover knowledge communities. Based on the community discovery result and system recommendation policy, we give our user the recommendation suggestions to help them to construct their personalized knowledge structure. The paper mainly introduce these algorithms, the AG algorithm based on aggregation and the KC algorithm based on K-Clique model, which are algorithms to discover knowledge communities in SGCL.

You might also be interested in these eBooks

Micro Nano Devices, Structure and Computing Systems

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 159)

Pages:

248-251

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.159.248

Citation:

Cite this paper

Online since:

December 2010

Authors:

Min Xu, Yuan Zhang, Mei Qi Fang, Ning Li

Keywords:

Community Discovery, Knowledge Group, Personalized Recommendation, Recommendation Policy

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] West D B: Introduction to Graph Theory [M]. Prentice Hall, Upper Saddle River, (2001).

Google Scholar

[2] Kernighan B W, Lin S: An efficient heuristic procedure for partitioning graphs [J]. Bell Systems Technical Journal, 1970. 49(2):, p.291~307.

DOI: 10.1002/j.1538-7305.1970.tb01770.x

Google Scholar

[3] Fiedler M: Algebraic connectivity of graphs [J]. Czechoslovak Mathematical Journal, 1973, 23(98), p.298~305.

DOI: 10.21136/cmj.1973.101168

Google Scholar

[4] Pothen A, Simon H, Liou K P: Partitioning sparse matrices with eigenvectors of graphs [J]. SIAM Journal on Matrix Analysis and Applications. 1990. 11(3), p.430~452.

DOI: 10.1137/0611030

Google Scholar

[5] Girvan M, Newman M E J: Community structure in social and biological networks [J]. Proc Natl Acad Sci, 2001, 99(12), pp.7821-7826.

DOI: 10.1073/pnas.122653799

Google Scholar