Book Recommendation to Wikipedia Article Readers in a University Library
Wikipedia has emerged as an important source of information for university students. It has been reported that university students tend to read and do research with Wikipedia articles more than they do with books, even when within a library. To encourage students to read library books as a more reliable source of information, a library system was developed for recommending library books to Wikipedia readers within a particular university library. The proposed system assigns a Nippon Decimal Classification (NDC) category to each Wikipedia article and recommends library books in the same NDC category to readers of the article. The recommended books are displayed to the reader with their covers and call numbers within that library. In the test implementation of the system, the precision of assigning NDC categories to Wikipedia articles using a convolutional neural network was as high as 87.4%, while the precision of selecting books for recommendation using a support vector machine reached 99.8%. In addition, 82.0% of the student subjects found at least one book that “I want to read” among three recommended books.
T. Anbiru et al., “Information Seeking Behavior,” Proceedings of the Spring Meeting of the Japan Society of Library and Information Science, 2010, pp. 87-90. (Text in Japanese.)
Japan Library Association, Statistics on library in Japan. Japan Library Association. 2018, 515p.
Y. Kim, “Convolutional Neural Networks for Sentence Classification,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014, pp. 1746-1751.
S. Arai and K. Tsuji, “Automatically Assigning NDC Categories to Reference Service Records by Using Machine Learning Methods,” Journal of the Japan Society of Information and Knowledge, vol. 25, no.1, 2015, pp. 23-40. (Text in Japanese.)
R. Johnson and T. Zhang, “Effective Use of Word Order for Text Categorization with Convolutional Neural Networks,” Proceedings of Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, 2015, pp. 103-112.
P. Wang et al., “Semantic Clustering and Convolutional Neural Network for Short Text Categorization,” Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015, pp. 352-357.
M. Mikawa et al., “Book Recommendation Signage System Using Silhouette-based Gait Classification,” Proceedings or the 10th International Conference on Machine Learning and Applications, 2011, pp. 416-419.
P. Jomsri, “FUCL Mining Technique for Book Recommender System in Library Service,” Proceedings of the 11th International Conference Interdisciplinarity in Engineering, 2018, pp. 550-557.
R. Mooney and L. Roy, “Content-based Book Recommending Using Learning for Text Categorization,” Proceedings of the 5th ACM conference on Digital Libraries, 2000, pp. 195-204.
S. Givon and V. Lavrenko, “Predicting Social-tags for Cold Start Book Recommendations,” Proceedings of the 3rd ACM Conference on Recommender Systems, 2009, pp. 333-336.
X. Yang et al., “ARTMAP-based Data Mining Approach and its Application to Library Book Recommendation,” Proceedings of the 2009 International Symposium on Intelligent Ubiquitous Computing and Education, 2009, pp. 26-29.
M.S. Pera et al., “Personalized Book Recommendations Created by Using Social Media Data,” Proceedings of the 2010 International Conference on Web Information Systems Engineering, 2010, pp. 390-403.
R.G. Crespo et al., “Recommendation System based on User Interaction Data Applied to Intelligent Electronic Books,” Computers in Human Behavior, vol. 27, 2011, pp. 1445-1449.
P.C. Vaz et al., “Improving a Hybrid Literary Book Recommendation System through Author Ranking,” Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, 2012, pp. 387-388.
C. Benkoussas and P. Bellot, “Book Recommendation based on Social Information,” Working Notes for CLEF 2013 Conference, 2013, pp. 23-26.
D. Pathak et al., “NOVA: Hybrid Book Recommendation Engine,” Proceedings of the IEEE 3rd International Advance Computing Conference, 2013, pp. 977-982.
P.C. Vaz et al., “Understanding Temporal Dynamics of Ratings in the Book Recommendation Scenario,” Proceedings of the 2013 International Conference on Information Systems and Design of Communication, 2013, pp. 11-15.
A.L. Garrido et al., “SOLE-R, a Semantic and Linguistic Approach for Book Recommendations,” Proceedings of the IEEE 14th International Conference on Advanced Learning Technologies, 2014, pp. 524-528.
M.S. Pera and Y. Ng, “Automating Readers’ Advisory to Make Book Recommendations for K-12 Readers,” Proceedings of the 8th ACM Conference on Recommender Systems, 2014a, pp. 9-16.
M.S. Pera and Y. Ng, “How Can We Help Our K-12 Teachers?: Using a Recommender to Make Personalized Book Suggestions,” Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2014b, pp. 335-342.
K. Priyanka et al., “Personalised Book Recommendation System based on Opinion Mining Technique,” Proceedings of the Global Conference on Communication Technologies, 2015, pp. 285-289.
S. Rajpurkar et al., “Book Recommendation System,” International Journal for Innovative Research in Science & Technology, vol. 1, no. 11, 2015, pp. 314-316.
A. Sase et al., “A Proposed Book Recommender System,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 2, 2015, pp. 481-483.
S. Bhosale et al., “SuggestABook: A Book Recommender Engine with Personality based Mapping,” International Journal of Computer Applications, vol. 159, no. 9, 2017, pp. 1-4.
B. Gao et al., “Research on the Application of Persona in Book Recommendation System,” Journal of Physics: Conference Series, vol. 910, 2017, pp. 1-8.
S.S. Sohail et al., “A Novel Approach for Book Recommendation Using Fuzzy based Aggregation,” Indian Journal of Science and Technology, vol. 10, no. 19, 2017, pp. 1-30.
H. Alharthi et al., “A Survey of Book Recommender Systems,” Journal of Intelligent Information Systems, vol. 51, 2018, pp. 139-160.
P. Parekh et al., “Web based Hybrid Book Recommender System Using Genetic Algorithm,” International Research Journal of Engineering and Technology, vol. 5, no. 8, 2018, pp. 1536-1539.
S.S. Sohail et al., “An OWA-based Ranking Approach for University Books Recommendation,” International Journal of Intelligent Systems, vol. 33, 2018, pp. 396-416.
T. Thanapalasingam et al., “The Smart Book Recommender: An Ontology-driven Application for Recommending Editorial Products,” Proceedings of the International Semantic Web Conference 2018, 2018, 5p. (No Pagination).
K. Tsuji et al., “Book Recommendation based on Library Loan Records and Bibliographic Information,” Proceedings of the 3rd International Conference on Integrated Information, 2013, 8p. (No Pagination).
K. Tsuji et al., “Book Recommendation Using Machine Learning Methods based on Library Loan Records and Bibliographic Information,” Proceedings of the 5th International Conference on E-Service and Knowledge Management, 2014, pp. 76-79.
K. Tsuji et al., “Book Recommendation Using Machine Learning Methods based on Library Loan Records and Bibliographic Information,” International Journal of Academic Library and Information Science, vol. 3, no. 1, 2015, pp. 7-23.
K. Tsuji, “Books Cited in Wikipedia: Possibility to Use their Nippon Decimal Classification Categories for Book Recommendation,” Proceedings of the 7th International Conference on E-Service and Knowledge Management, 2016, pp. 1196-1197.
K. Tsuji, “Automatic Classification of Wikipedia Articles by Using Convolutional Neural Network,” Proceedings of the 9th Qualitative and Quantitative Methods in Libraries International Conference, 2017, 8p. (No Pagination).
Mecab. http://taku910.github.io/mecab/ [Last Access: 2019-01-14]
mecab-ipadic-NEologd: neologism dictionary for MeCab. https://github.com/neologd/mecab-ipadic-neologd [Last Access: 2019-01-14]
Word2vec. https://radimrehurek.com/gensim/models/word2vec.html [Last Access: 2019-01-14]
gensim: topic modelling for humans. https://radimrehurek.com/gensim/ [Last Access: 2019-01-14]
OpenSearch by the National Diet Library of Japan. http://iss.ndl.go.jp/information/api/ [Last Access: 2019-01-14]
Tensorflow. https://www.tensorflow.org/ [Last Access: 2019-01-14]
LIBSVM: library for support vector machines. https://www.csie.ntu.edu.tw/~cjlin/libsvm/ [Last Access: 2019-01-14]
OpenBD. https://openbd.jp/ [Last Access: 2019-01-14]
R.K. Merton, “The Matthew Effect in Science,” Science, vol. 159, no. 3810, 1968, pp. 56-63.