- Model-based and memory-based collaborative filtering algorithms for complex knowledge models
- Number of pages
- Amsterdam: University of Amsterdam, Human Computer Studies Laboratory
- DynaLearn deliverable
- Volume | Edition (Serie)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
In DynaLearn, learners, teachers and domain experts create Qualitative Reasoning
(QR) conceptual models that may store in a common repository. These models represent a valuable
source of knowledge that could be used to assist new users in the creation of models with related
topics. However, finding the appropriate models for this knowledge reuse can be a difficult task as
the amount of models in the repository increases.
This document describes the task of recommending relevant models from the repository and its
integration with the generation of semantic feedback. The recommendation process integrates both
model-based and memory-based collaborative filtering algorithms. The recommended models are used as reference sources and are compared with the learner model. From the analysis of the differences between the models, a list of suggestions is generated and provided to the learner as feedback.
Finally, the document includes an appendix describing the User Management
System and how the models in the repository can be organized in courses. These
courses play an important role in the recommendation of models.