@article{oai:tsukuba.repo.nii.ac.jp:00032224,
 author = {辻, 慶太 and 芳鐘, 冬樹 and 逸村, 裕 and Tsuji, Keita and Yoshikane, Fuyuki and Sato, Sho and Itsumura, Hiroshi},
 issue = {1},
 journal = {International Journal of Academic Library and Information Science},
 month = {Jan},
 note = {In this paper, we propose a method to recommend Japanese books to university students through
machine learning modules based on several features, including library loan records. We determine the
most effective method among the ones that used (a) a support vector machine (SVM), (b) a random
forest, and (c) Adaboost. Furthermore, we assess the most effective combination of relevant features
among (1) the association rules derived from library loan records, (2) book titles, (3) Nippon Decimal
Classification (NDC) categories, (4) publication years, and (5) frequencies with which books were
borrowed. We conducted an experiment involving 60 subjects who were students at T University. The
books recommended by our candidate methods as well as the loan records used were obtained from the
T University library. The results showed that books recommended by the method that employs an SVM
based on features (2), (3), and (5) were rated most favorably by subjects. The method outperforms
previous book recommendation techniques, such as that proposed by Tsuji et al. (2013), and is
comparable in recommendation performance to the website Amazon.co.jp. The results obtained were
independent of student grades, NDC categories, and the publication years of books.},
 pages = {7--23},
 title = {Book Recommendation Using Machine Learning Methods Based on Library Loan Records and Bibliographic Information},
 volume = {3},
 year = {2015}
}