A personalized recommendation method using a tagging ontology for a social E-learning system

Authors:
Hyon Hee Kim
Affiliations:
Department of Information and Statistics, Dongduk Women's University, Sungbuk-Gu, Seoul, South Korea
Venue:
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Year:
2011

Citing 7
Cited 2

Usage patterns of collaborative tagging systems

Journal of Information Science
Tag-based social interest discovery

Proceedings of the 17th international conference on World Wide Web
Co-Annotea: A System for Tagging Relationships Between Multiple Mixed-Media Objects

IEEE MultiMedia
On Social e-Learning

ICWL '009 Proceedings of the 8th International Conference on Advances in Web Based Learning
On social Web sites

Information Systems
Representing and sharing folksonomies with semantics

Journal of Information Science
Extending a hybrid tag-based recommender system with personalization

Proceedings of the 2010 ACM Symposium on Applied Computing

Generation of tag-based user profiles for clustering users in a social music site

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
The effect of users' tagging motivation on the enlargement of digital educational resources metadata

Computers in Human Behavior

Quantified Score

Hi-index	0.00

Visualization

Abstract

As collaborative tagging has become increasingly popular, its role in social e-learning systems has attracted much attention. In this paper, we present a method to provide personalized recommendation services using a tagging ontology for a social e-learning system called TagSES. The TagSES ontology models relationships among students, lecture materials and tags, and the tags are mapped to the domain ontology. Based on the reasoning rules, students with similar interests are clustered and relevant lecture materials are recommended. To validate our method, we have implemented a prototype social e-learning system for a music history domain.