Diverse reports recommendation system based on latent Dirichlet allocation

Abstract

This paper presents a proposal for system supporting learners in improving their report-writing skills by recommending reports from previous learners. The proposed system recommends reports that share similar subjects but which have different structures, expressions, and originality based on the distributions of words and subjects within the reports, as estimated using latent Dirichlet allocation (LDA). An important assumption made for this study is that reports with different word distributions tend to include different structures, expressions, and originality when they share similar subjects. Based on that assumption, the system selects and recommends reports that have dissimilar word distributions but which share similar subject distributions with a learner’s report. The proposed system is expected to enhance learning of various writing skills from other learners. Finally, this paper demonstrates the effectiveness of the proposed system through actual data experiments.