Enhanced Collaborative Filtering to Recommender Systems of Technology Enhanced Learning

Recommender Systems (RSs) are largely used nowadays in many ar- eas to generate items of interest to users. Recently, they are applied in the Technology Enhanced Learning (TEL) field to let recommending relevant learning resources to support teachers or learners' need. In this paper we pro- pose a novel recommendation technique that combines a fuzzy collaborative fil- tering algorithm with content based one to make better recommendation, using learners' preferences and importance of knowledge to recommend items with different context in order to alleviate the Stability vs. Plasticity problem of TEL Recommender Systems. Empirical evaluations show that the proposed tech- nique is feasible and effective.

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