Simulated Analysis of MAUT Collaborative Filtering for Learning Object Recommendation

This paper examines the case of developing a learning resources collaborative filtering service for an online community of teachers in Europe. A data set of multi-attribute evaluations of learning resources has been collected from the teachers who used European Schoolnet’s CELEBRATE portal. Using this data set as input, a candidate multi-attribute utility collaborative filtering algorithm is appropriately parameterized and tested for potential implementation on the portal. This simulation experiment may serve as a first step towards the understanding and appropriate specialization of a collaborative filtering service for the given user community.

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