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An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem

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Abstract

An e-learning recommender system (RS) aims to generate personalized recommendations based on learner preferences and goals. The existing RSs in the e-learning domain still exhibit drawbacks due to its inability to consider the learner characteristics in the recommendation process. In this paper, we are dealing with the new user cold-start problem, which is a major drawback in e-learning content RSs. This problem can be mitigated by incorporating additional learner data in the recommendation process. This paper proposes an ontology-based (OB) content recommender system for addressing the new user cold-start problem. In the proposed recommendation model, ontology is used to model the learner and learning objects with their characteristics. Collaborative and content-based filtering techniques are used in the recommendation model to generate the top N recommendations based on learner ratings. Experiments were conducted to evaluate the performance and prediction accuracy of the proposed model in cold-start conditions using the evaluation metrics mean absolute error, precision and recall. The proposed model provides more reliable and personalized recommendations by making use of ontological domain knowledge.

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Data availability

The datasets used and/or analyzed during the current study are available from the authors on reasonable request.

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  1. Division of Information Technology, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, India

    Joy Jeevamol & V. G. Renumol

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Jeevamol, J., Renumol, V.G. An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Educ Inf Technol 26, 4993–5022 (2021). https://doi.org/10.1007/s10639-021-10508-0

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