Fuzzy inference for Learning Object Recommendation

In this paper a Learning Object Recommendation system is proposed. Learning Objects (LOs) in this context are reusable Web based resources (i.e. a web page, a video or images) that support some learning activity. The system follows a hybrid approach, combining two collaborative filtering (CF) algori...

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Bibliographic Details
Main Authors: Garcia-Valdez, M, Alanis, A, Parra, B
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric measurements
Collaboration
Correlation
Input variables
Particle measurements
Prediction algorithms
Recommender systems
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Summary:In this paper a Learning Object Recommendation system is proposed. Learning Objects (LOs) in this context are reusable Web based resources (i.e. a web page, a video or images) that support some learning activity. The system follows a hybrid approach, combining two collaborative filtering (CF) algorithms and a fuzzy inference system (FIS) defined by the instructor. This allows the instructor to adopt the role of facilitator, making recommendations when necessary, but allowing students to work together whenever possible. We propose that the final recommendation assigned to a LO, is the weighted average of the three models: Instructor, Profile and Correlation. Finally another FIS is used to determine the weights of these recommendations, the assignment of weights aims to compensate for some of the shortcomings of collaborative filtering algorithms. An experimental evaluation of this approach is presented.
ISSN:1098-7584
DOI:10.1109/FUZZY.2010.5584322