KeywordsComputer and information sciences
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AbstractImportant aspect in the modern e-learning systems is selecting the most adequate learning materials based on learners’ requirements, needs and knowledge goals. Recommender systems based on collaborative filtering contribute to overcoming the information overload in personalized learning environments. That’s why there is imminent need of using systems that have the capability to detect the learners’ needs and to recommend them the most adequate learning context. In recent years, it is common practice to use tags in the process of filtering the most useful learning materials.Through the tagging, learners can mark or highlight some learning materials and can contribute to organizing and retrieving useful learning materials. Our previous researches were focused on tag-based collaborative filtering and learning style determination, the factors that affect the tag-based collaborative filtering, in order to suggest useful learning material in adequate format. In this paper, we propose a new tag-based collaborative algorithm that takes in consideration the factors that affect the tag-based collaborative filtering in order to develop more efficient and accurate algorithm, and suggest the learning materials based on posted tags rating and students rating. The developed system was implemented at the Faculty of Law – Bitola, and the evaluation results are shown in this paper.
Kotevski, Aleksandar and Martinovska Bande, Cveta (2014) Improved algorithm for tag-based collaborative filtering. A Journal for Information Technology, Education Development and Teaching Methods of Technical and Natural Sciences, 4 (1). pp. 1-7. ISSN ISSN 2217-7949