International Journal of Academic Library and Information Science
 

International Journal of Academic Library and Information Science

Vol. 3(1), pp. 7–23, January. 

ISSN: 2360-7858

DOI: 10.14662/IJALIS2014.046

Full Length Research

Book Recommendation Using Machine Learning Methods Based on Library Loan Records and Bibliographic Information

Keita Tsuji^1)*), Fuyuki Yoshikane²⁾, Sho Sato³⁾, and Hiroshi Itsumura⁴⁾

^{1, 2, 4} Faculty of Library, Information and Media Science, University of Tsukuba, Kasuga, Tsukuba City, Ibaraki-ken 305-8550, Japan, Phone & Fax: +81-29-859-{1428 ¹⁾, 1346 ²⁾, 1274 ⁴⁾}

³ Faculty of Social Studies, Doshisha University, Karasuma Higashi-iru, Imadegawa-dori, Kamigyo-ku, Kyoto 602-8580, Japan. E-mail: min2fly@gmail.com.  Phone & Fax: +75-251-3454

¹*Corresponding author’s E-mail: keita@slis.tsukuba.ac.jp

Accepted 30 December 2014

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.

Keywords: Book Recommendation; Recommender System; Library Loan Records; Support Vector Machine (SVM); Random Forest; Adaboost


 

Cite This Article As: Tsuji K, Yoshikane F, Sato S, Itsumura H (2015). Book Recommendation Using Machine Learning Methods Based on Library Loan Records and Bibliographic Information. Inter. J. Acad. Lib. Info. Sci. 3(1): 7-23.

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