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A hybrid recommender system for e-learning based on context awareness and sequential pattern mining

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Abstract

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61370137), the National Basic Research Program of China (No. 2012CB7207002), the Ministry of Education—China Mobile Research Foundation Project (Nos. 2015/5-9 and 2016/2-7) and the 111 Project of Beijing Institute of Technology.

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Authors and Affiliations

  1. School of Computer Science and Technology Beijing Institute of Technology, Beijing, 100081, China

    John K. Tarus & Zhendong Niu

  2. Directorate of ICT, Moi University, Eldoret, Kenya

    John K. Tarus

  3. School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA

    Zhendong Niu

  4. University of Science and Technology Beijing, Beijing, 100083, China

    Dorothy Kalui

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  1. John K. Tarus
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  2. Zhendong Niu
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  3. Dorothy Kalui
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Correspondence to Zhendong Niu.

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The authors declare that they have no conflict of interest.

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Communicated by A. Di Nola.

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Tarus, J.K., Niu, Z. & Kalui, D. A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Comput 22, 2449–2461 (2018). https://doi.org/10.1007/s00500-017-2720-6

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