Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain

Authors:
Leyla Zhuhadar;Olfa Nasraoui;Robert Wyatt;Elizabeth Romero
Affiliations:
-;-;-;-
Venue:
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2009

Citing 10
Cited 1

Recommender systems in e-commerce

Proceedings of the 1st ACM conference on Electronic commerce
Automatic personalization based on Web usage mining

Communications of the ACM
Introduction to Modern Information Retrieval

Introduction to Modern Information Retrieval
Evaluation of hierarchical clustering algorithms for document datasets

Proceedings of the eleventh international conference on Information and knowledge management
Optimizing search engines using clickthrough data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building a Recommender Agent for e-Learning Systems

ICCE '02 Proceedings of the International Conference on Computers in Education
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Search Engines that Learn from Implicit Feedback

Computer
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites

IEEE Transactions on Knowledge and Data Engineering
Semantic Information Retrieval for Personalized E-Learning

ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01

A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a multi-model ontology-based framework for semantic search of educational content in E-learning repository of courses, lectures, multimedia resources, etc. This hybrid recommender system is driven by two types of recommendations: content-based (domain ontology model) and rule-based (learner’s interest-based and cluster-based). The domain ontology is used to represent the learning materials. In this context, the ontology is composed by a hierarchy of concepts and sub-concepts. Whereas, the learner’s ontology model represents a subset of the domain ontology, and the cluster-based recommendations are added as additional semantic recommendations to the model. Combining the content-based with the rule-based provides the user with hybrid recommendations. All of them influenced the re-ranking of the retrieved documents with different weights. Our proposed approach has been implemented on the HyperManyMedia1 platform.