School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
School of Education, Victoria University of Wellington, Wellington, New Zealand.
PLoS One. 2021 Jan 22;16(1):e0245485. doi: 10.1371/journal.pone.0245485. eCollection 2021.
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.
大规模开放在线课程（MOOCs）在过去几年中越来越受欢迎。在线学习资源的空间呈指数级增长，这造成了信息过载的问题。为了克服这个问题，已经提出了推荐系统，这些系统可以根据用户的兴趣向他们推荐学习资源。MOOCs 包含大量的数据，随着新学习者的注册，数据量不断增加。传统的推荐技术存在可扩展性、稀疏性和冷启动问题，导致推荐质量较差。此外，它们无法适应新数据到达时模型的增量更新，因此不适合 MOOCs 的动态环境。基于这一研究思路，我们提出了一种新颖的在线推荐系统，即 NoR-MOOCs，它具有准确性、良好的可扩展性，并且克服了之前推荐系统中存在的问题。通过在 COCO 数据集上进行的广泛实验，我们从经验上证明，在预测和分类准确性指标方面，NoR-MOOCs 明显优于传统的 KMeans 和协同过滤算法。
