Learning Resource Recommendation: An Orchestration of Content-Based Filtering, Word Semantic Similarity and Page Ranking

Technologies supporting online education have been abundantly developed recent years. Many repositories of digital learning resources have been set up and many recommendation approaches have been proposed to facilitate the consummation of learning resources. In this paper, we present an approach that combines three recommendation technologies: content-based filtering, word semantic similarity and page ranking to make resource recommendations. Content-based filtering is applied to filter syntactically learning resources that are similar to user profile. Word semantic similarity is applied to consolidate the content-based filtering with word semantic meanings. Page ranking is applied to identify the importance of each resource according to its relations to others. Finally, a hybrid approach that orchestrates these techniques has been proposed. We performed several experiments on a public learning resource dataset. Results on similarity values, coverage of recommendations and computation time show that our approach is feasible.