Rancang Bangun Recommender System dengan Menggunakan Metode Collaborative Filtering untuk Studi Kasus Tempat Kuliner di Surabaya
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Dec 1, 2015
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Anthea Adellya Pradnya Devi
David Boy Tonara
Abstract
Culinary will have more and more big opportunities for the people who want to have a new business. Because food and beverages are people’s primary requirements. Even now, culinary spot not only to fulfill primary requirement, but also become the place where we can meet or chill out with friends , and business meeting. Nowadays opening a culinary business becoming a trend for people who going to start a new business. We can see people eat and drink every time everywhere. Even now a restaurant or cafe can become not only a place to eat, but to hang out, have a meeting, etc. This causes the culinary business to grow rapidly and has so much variety in its product. However, those sometimes make people confuse where is the best place to eat that suit their preferences. Therefore a recommender system website with collaborative filtering method has been built, to help consumer pick a restaurant that suit their wants, according to other’s consumer rating that have been processed with item-based collaborative filtering algorithm. The experiment about recommendations for help people making decisions that suit their preferences have value of accuracy around 76% based on 32 sampling data.
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Devi, A. A. P., & Tonara, D. B. (2015). Rancang Bangun Recommender System dengan Menggunakan Metode Collaborative Filtering untuk Studi Kasus Tempat Kuliner di Surabaya. Jurnal Informatika Dan Sistem Informasi, 1(2), 102–112. Retrieved from https://journal.uc.ac.id/index.php/JUISI/article/view/82
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References
[1] Kotler, P., Armstrong, G., Saunders, J. and Wong, V. (2002). Principles of Marketing, 3rd European Edition. London: Prentice Hall.
[2] McGinty, L. and Smyth, B. (2006). Adaptive selection: analysis of critiquing and preference based feedback in conversational recommender systems. International Journal of Electronic Commerce, 11(2), pp 35–57.
[3] Han, J. dan Kamber, M. (2006). Data Mining: Concepts and Techniques, 2nd Edition. Elsevier Inc.
[4] Haskett, M. (2000). An intro to data mining: Analyzing the tools and techniques. Enterprise Systems Journal, May, 34-39.
[5] Linden, G. et al. (2003). Amazon.com recommendations item-to-item Collaborative Filtering. The IEEE Computer Society.
[6] Li, Qing and Kim, Byeong Man. (2002). An Approach for Combining Content-based and Collaborative Filters. Department of Computer Science, Kumoh National Institute of Technology. [7] Adomavicius, G., and Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A survey of the state-of-the-art and possible extensions, University of Minnesota, Minnieapolis.
[8] Sarwar, BM., Karypis, G., Konstan, JA., and Riedl, TJ. (2001). Item-Based Collaborative Filtering recommendation algorithms, Proceeding of 10th International World Wide Web Conference, ACM Press.
[9] Schein AI., Popescul A., Ungar LH., and Pennock DM. (2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th annual international ACM SIGIR conference.
[10] Brady, M., and Loonam, J. (2010). Exploring the use of entity-relationship diagramming as a technique to support grounded theory inquiry. Bradford: Emerald Group Publishing.
[2] McGinty, L. and Smyth, B. (2006). Adaptive selection: analysis of critiquing and preference based feedback in conversational recommender systems. International Journal of Electronic Commerce, 11(2), pp 35–57.
[3] Han, J. dan Kamber, M. (2006). Data Mining: Concepts and Techniques, 2nd Edition. Elsevier Inc.
[4] Haskett, M. (2000). An intro to data mining: Analyzing the tools and techniques. Enterprise Systems Journal, May, 34-39.
[5] Linden, G. et al. (2003). Amazon.com recommendations item-to-item Collaborative Filtering. The IEEE Computer Society.
[6] Li, Qing and Kim, Byeong Man. (2002). An Approach for Combining Content-based and Collaborative Filters. Department of Computer Science, Kumoh National Institute of Technology. [7] Adomavicius, G., and Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A survey of the state-of-the-art and possible extensions, University of Minnesota, Minnieapolis.
[8] Sarwar, BM., Karypis, G., Konstan, JA., and Riedl, TJ. (2001). Item-Based Collaborative Filtering recommendation algorithms, Proceeding of 10th International World Wide Web Conference, ACM Press.
[9] Schein AI., Popescul A., Ungar LH., and Pennock DM. (2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th annual international ACM SIGIR conference.
[10] Brady, M., and Loonam, J. (2010). Exploring the use of entity-relationship diagramming as a technique to support grounded theory inquiry. Bradford: Emerald Group Publishing.