Pemodelan personalisasi rekomendasi buku dengan pendekatan association rule mining

https://doi.org/10.22146/bip.36007

Sely Yoanda(1*), Imas Sukaesih Sitanggang(2), Agus Buono(3)

(1) Fakultas MIPA, Institut Pertanian Bogor, Bogor
(2) Fakultas MIPA, Institut Pertanian Bogor, Bogor
(3) Fakultas MIPA, Institut Pertanian Bogor, Bogor
(*) Corresponding Author

Abstract


Introduction. Library X is an academic library in Jakarta, Indonesia. Library X has provided Online Public Access Catalog (OPAC) as a tool to provide information related to the collection. However, sometimes the information appears does not show high relevancy. One way to solve this problem is to develop user need based-book recommendation system. The purpose of this study is to create personalization model of book recommendations in Library X.

Data Collection Method. The method used in this study was association rule mining using Apriori algorithm.

Results and Discussions. The results showed that the book relationships for the minimum support was 0.1% and the minimum confidence was 10% and generated 42 association rules. It is noted that 657 (Accounting) and 658 (Management) are found to support for 2.6% with the confidence level for 14%.

Conclusions. Book recommendation is formulated by selecting the rule with maximum support and confidence. The recommendation system is designed to be integrated to web application and user’s e-mail.


Keywords


Apriori Algorithm;Association Rule Mining;Book Recommendation;Library Transaction Data;Personalization

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References

Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference (pp. 487–499). Santiago: Institute of Electrical and Electronics Engineers. https://doi.org/10.1007/BF02948845

Crespo, R. G., Martínez, O. S., Lovelle, J. M. C., García-Bustelo, B. C. P., Gayo, J. E. L., & Pablos, P. O. De. (2011). Recommendation system based on user interaction data applied to intelligent electronic books. Computers in Human Behavior, 27(4), 1445–1449. https://doi.org/10.1016/j.chb.2010.09.012

Han, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techiques (3rd ed.). Waltham: Morgan Kaufman.

Jomsri, P. (2014). Book recommendation system for digital library based on user profiles by using association rule. In Innovative Computing Technology (pp. 130–134). Luton: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/INTECH.2014.6927766

Li, J., & Chen, P. (2008). The application of association rule in library system. In International Symposium on Knowledge Acquisition and Modeling Workshop Proceedings (pp. 248–251). Wuhan, China: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/KAMW.2008.4810472

Masruri, F., & Mahmudy, W. F. (2007). Personalisasi web e-commerce menggunakan recommender system dengan metode item-based collaborative filtering. Kursor, 3(1), 1–12. Retrieved Mei 14, 2017, from http://wayanfm.lecture.ub.ac.id/files/2014/03/200701-Kursor-Farid-Wayan-Recommender-System.pdf

Rajpurkar, S., Bhatt, D., & Malhotra, P. (2015). Book recommendation system. International for Innovative Research in Science and Technology, 1(11), 314–316. Retrieved Mei 14, 2017, from www.ijirst.org/articles/IJIRSTV1I11135.pdf

Sitanggang, I. S., Husin, N. A., Agustina, A., & Mahmoodian, N. (2010). Sequential pattern mining on library transaction data. In International Symposium on Information Technology (pp. 1–4). Kuala Lumpur, Malaysia: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITSIM.2010.5561316

Tan, P.-N., Steinbach, M., & Kumar, V. (2014). Introduction to data mining (1st ed.). Harlow: Pearson Education Limited.

Tsuji, K., Takizawa, N., Sato, S., Ikeuchi, U., Ikeuchi, A., Yoshikane, F., & Itsumura, H. (2014). Book recommendation based on library loan records and bibliographic information. Procedia-Social and Behavioral Sciences, 147, 478–486. https://doi.org/10.1109/IIAI-AAI.2014.26

Wandi, N., Hendrawan, R. A., & Mukhlason, A. (2012). Pengembangan sistem rekomendasi penelusuran buku dengan penggalian association rule menggunakan algoritma apriori (studi kasus Badan Perpustakaan dan Kearsipan Provinsi Jawa Timur). Jurnal Teknik ITS, 1(1), 1–5. https//doi.org/10.12962/j23373539.v1i1.1293

Xin, L., Haihong, E., Junde, S., Meina, S., & Junjie, T. (2013). Collaborative book recommendation based on readers’ borrowing records. In International Conference on Advanced Cloud and Big Data (pp. 159–163). Nanjing, China: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CBD.2013.14

Zhu, Z., & Wang, J.-Y. (2007). Book recommendation service by improved association rule mining algorithm. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics (pp. 3864–3869). Hong Kong, China: Institute of Electrical and Electronis Engineers. https://doi.org/10.1109/ICMLC.2007.4370820



DOI: https://doi.org/10.22146/bip.36007

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