Contextual information retrieval within recommender system: Case study "e-learning system"

Abstract

This paper focuses on monitoring and analyzing user activities on collaborative filtering-based recommender system in order to guess suitable and unsuitable items' context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition...) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.