Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)
Khalid, A., Lundqvist, K., Yates, A. and Ghazanfar, M. 2021. Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs). PLoS ONE. 16 (Art. e0245485). https://doi.org/10.1371/journal.pone.0245485
|Khalid, A., Lundqvist, K., Yates, A. and Ghazanfar, M.
Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics.
|16 (Art. e0245485)
|Public Library of Science
File Access Level
|Digital Object Identifier (DOI)
|22 Jan 2021
|Publication process dates
|02 Jan 2021
|26 Jan 2021
|© 2021 The Authors
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