Edu-mining for Book Recommendation for Pupils
Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa, Koichiro Morihiro · Educational Data Mining · 2009
This paper proposes a novel method for recommending books to pupils based on a framework called Edu-mining. One of the properties of the proposed method is that it uses only loan histories (pupil ID, book ID, date of loan) whereas the conventional methods require additional information such as taste information from a great number of users which is costly to obtain. To achieve this, the proposed method solves the book recommendation problem as a problem of loan date prediction, relying solely on loan histories. Experiments show that the proposed method achieves an accuracy of 60% and outperforms the method (weighted slope open collaborative filtering) used for comparison. In addition to the performance, the proposed method has the following two advantages: (i) it is inexpensive compared to the conventional methods and (ii) reading level is adjustable.