Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
International Journal of Learning Technology
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Unified collaborative filtering model based on combination of latent features
Expert Systems with Applications: An International Journal
Courseware recommendation in e-learning system
ICWL'06 Proceedings of the 5th international conference on Advances in Web Based Learning
Modeling long term learning of generic skills
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A hybrid user-centred recommendation strategy applied to repositories of learning objects
International Journal of Web Based Communities
Applicability of data mining algorithms for recommendation system in e-learning
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
e-learning recommender system for learners in online social networks through association retrieval
Proceedings of the CUBE International Information Technology Conference
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges
IEEE Transactions on Learning Technologies
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Explosive growth of e-learning in the recent years has caused difficulty of locating appropriate learning resources to learners in these environments. Recommender system is a promising technology in e-learning environments to present personalized offers and deliver suitable learning resources for supporting activity of users. Compared with resource recommendation in e-commerce systems, users in e-learning systems have topic preferences in e-learning systems. However, e-learning systems have their own characteristics and current e-commerce algorithms cannot effectively use these characteristics to address needs of recommendations in these environments. To address requirement of e-learning resource recommendation, this research uses attribute of resources and learners and the sequential patterns of the learner's accessed resource in recommendation process. Learner Tree (LT) is introduced to take into account explicit multi-attribute of resources, time-variant multi-preference of learner and learners' rating matrix simultaneously. Implicit attributes are introduced and discovered using matrix factorization. BIDE algorithm also is used to discover sequential patterns of resource accessing for improving the recommendation quality. Finally, the results recommendation of implicit and explicit attribute based collaborative filtering and BIDE are combined. The experiments show that our proposed method outperforms the previous algorithms on precision and recall measures and the learner's real learning preference can be satisfied accurately according to the real-time up dated contextual information.