Fab: content-based, collaborative recommendation
Communications of the ACM
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
ACM Transactions on Information Systems (TOIS)
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Fuzzy computational models for trust and reputation systems
Electronic Commerce Research and Applications
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Improving learning management through semantic web and social networks in e-learning environments
Expert Systems with Applications: An International Journal
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With the fast growing learning materials and resources in online communities, it is quite challenging to find suitable materials or resources based on learner's preferences including their learning styles and knowledge levels. E-learning recommender system (eLRS) deals with this information overload by providing valuable resources to learners. Since social influence plays an important role in online virtual communities, we are attempting to consider it in the framework of e-learning recommender systems. In this paper, we propose association retrieval based resource recommendation to learners in online social networks. In our system, learners share their resource ratings to their friends in online social network. The similarity between a pair of friends is derived from their mutual ratings' history and their preferences. A learner asks a resource rating query to his instant and distant friends through the social network and based on their query responses, association retrieval technique is employed to infer recommendation score for the resource for that learner. Experimental results reveal that our proposed system not only outperforms the other benchmark algorithms but also handles the data sparsity concern inherent in collaborative filtering.