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A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining

Research paper by John K. Tarus, Zhendong Niu, Abdallah Yousif

Indexed on: 23 Mar '17Published on: 02 Mar '17Published in: Future Generation Computer Systems



Abstract

In recent years, there has been significant growth in the use of online learning resources by learners. However, due to information overload, many learners are experiencing difficulties in retrieving useful and relevant learning resources that meet their learning needs. Although existing recommender systems have recorded significant success in e-commerce domain, they still experience drawbacks in making accurate recommendations of learning resources in e-learning domain due to differences in learner characteristics such as learning style, knowledge level as well as learners’ sequential learning patterns. Most of the existing recommendation techniques do not consider differences in learner characteristics. This problem can be alleviated through incorporation of additional information about the learner into the recommendation process. Furthermore, many recommendation techniques experience cold-start and rating sparsity problems. In this paper, we propose a hybrid knowledge-based recommender system based on ontology and sequential pattern mining (SPM) for recommendation of e-learning resources to learners. In the proposed recommendation approach, ontology is used to model and represent the domain knowledge about the learner and learning resources whereas SPM algorithm discovers the learners’ sequential learning patterns. Our approach involves four steps: (1) creating ontology to represent knowledge about the learner and learning resources, (2) computing ratings similarity based on ontology domain knowledge and making predictions for the target learner, (3) generation of top N learning items by the collaborative filtering recommendation engine, and (4) application of SPM algorithm to the top N learning items to generate the final recommendations for the target learner. A number of experiments were carried out to evaluate the proposed hybrid recommender system and results show improved performance. Furthermore, the proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.

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