Designing a learning objects recommendation system for repositories based on user's perception: The Rodas case.
Abstract
This paper describes a Learning Objects (LO) Recommendation System (RS) for repositories. The system is based on collaborative filtering using an adaptation of k-neighboring algorithm which is supported on user´s perception about usability and usefulness rather than downloading LO from repository. It also shows how the k-neighboring algorithm is adapted to user´s perception by implementing a voting system of LO. Finally, the RS validation using RODAS repository is given describing some pieces of algorithm and the computational model.
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