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Issue No.04 - Oct.-Dec. (2013 vol.6)
pp: 337-349
Mohamed Amine Chatti , Learning Technol., RWTH Aachen Univ., Aachen, Germany
Simona Dakova , Learning Technol., RWTH Aachen Univ., Aachen, Germany
Hendrik Thus , Learning Technol., RWTH Aachen Univ., Aachen, Germany
Ulrik Schroeder , Learning Technol., RWTH Aachen Univ., Aachen, Germany
The personal learning environment (PLE) concept offers a learner-centric view of learning and suggests a shift from knowledge-push to knowledge-pull approach to learning. One concern with a PLE-driven knowledge-pull approach to learning, however, is information overload. Recommender systems can provide an effective mechanism to deal with the information overload problem in PLEs. In this paper, we study different tag-based collaborative filtering recommendation techniques on their applicability and effectiveness in PLE settings. We implement 16 different tag-based collaborative filtering recommendation algorithms, memory based as well as model based, and compare them in terms of accuracy and user satisfaction. The results of the conducted offline and user evaluations reveal that the quality of user experience does not correlate with high-recommendation accuracy.
Collaboration, Recommender systems, Performance evaluation, Performance evaluation,user evaluation, PLE, recommender systems, collaborative filtering, offline evaluation
Mohamed Amine Chatti, Simona Dakova, Hendrik Thus, Ulrik Schroeder, "Tag-based collaborative filtering recommendation in personal learning environments", IEEE Transactions on Learning Technologies, vol.6, no. 4, pp. 337-349, Oct.-Dec. 2013, doi:10.1109/TLT.2013.23
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