Leveraging Semantic Knowledge Graphs in Educational Recommenders to Address the Cold-Start Problem

In informational recommenders, significant challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to make advances toward building a semantically aware educational recommendation system, where the aim is to estimate the knowledge/interests of learners to leverage suitable recommendations. To do so, our proposed model incorporates notions of semantic relatedness between knowledge topics, propagating latent information across those semantically related topics. To introduce this novel learner model that exploits semantic relatedness, we make use of the Wikipedia link graph. Our final aim is to better predict learner engagement and latent knowledge in a lifelong learning scenario and evaluate how semantic knowledge graphs can facilitate this task. Our proposal, Semantic TrueLearn, one of the first attempts at fusing probabilistic …