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http://dx.doi.org/10.7840/kics.2016.41.7.768

Personalized Group Recommendation Using Collaborative Filtering and Frequent Pattern  

Kim, Jung Woo (Mapssi, Co., Ltd.)
Park, Kwang-Hyun (Kwangwoon University School of Robotics)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.41, no.7, 2016 , pp. 768-774 More about this Journal
Abstract
This paper deals with a method to recommend the combination of items as a group according to similarity to handle application area such as fashion and cooking, while the previous methods recommend single item such as a book, music or movie. Collaborative filtering is a method to recommend an item selected by users with similar tendency based on similarity between users. In this paper, the proposed method generates a set of frequent items based on collaborative filtering and association rules and recommends a group by similarity between groups. To show the validity of the proposed method, experiments are performed with purchase data collected from e-commerce for four months.
Keywords
Collaborative; filtering; association rule; frequent pattern; group recommendation; similarity;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, "Collaborative filtering recommender systems," Foundations and Trends in Human-Computer Interaction, vol. 4, no. 2, pp. 81-173, 2010.
2 J. Moon, I. Jang, Y. C. Choe, J. G. Kim, and G. Bock, "Case study of big data-based agri-food recommendation system according to types of customers," J. KICS, vol. 40, no. 5, pp. 903-913, 2015.   DOI
3 D.-J. Seo and T.-S. Kim, "Influence of personal information security vulnerabilities and perceived usefulness on bank customers' willingness to stay," J. KICS, vol. 40, no. 8, pp. 1577-1587, 2015.   DOI
4 T.-N. Phan and M. Yoo, "Facebook fan page evaluation system based on user opinion mining," J. KICS, vol. 40, no. 12, pp. 2488- 2490, 2015.   DOI
5 P. Harrington, Machine Learning in Action, Manning, 2012.
6 D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Commun. ACM, vol. 35, no. 12, pp. 61-70, Dec. 1992.
7 Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," IEEE Computer, pp. 42-49, Aug. 2009.
8 B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proc. 10th Int. Conf. World Wide Web, pp. 285-295, 2001.
9 X. Su and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in Artificial Intelligence, vol. 2009, no. 4, pp. 1-19, Jan. 2009.
10 P. Cremonesi, R. Turrin, E. Lentini, and M. Matteucci, "An evaluation methodology for collaborative recommender systems," in Proc. International Conf. Automated solutions for Cross Media Content and Multi-channel Distribution, pp. 224-231, 2008.
11 R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th Int. Conf. Very Large Data Bases, pp. 487-499, 1994.