レビュー情報を用いた学術本の難易度推定

概要

論文の詳細を見る
A collaborate filtering has been generally used as a method which recommends items to customers. However, recommending academic books, it need to consider difficulty of them and individual amount of knowledge as well as user's preference. If the recommendation method considers only user's preference, they might regret after buying or reading recommended book because it won't match user's appropriate level. In this paper, we focus on academic books and propose a method which estimates the difficulty of academic books using user's reviews. Estimating difficulty of books will support users to search and recommend academic books that match user's skill. Moreover, we evaluated applying our method to academic text books about C programming Language. We verified that our method is more effective than traditional methods for academic books.
人工知能学会 = The Japanese Society for Artificial Intelligenceの論文