Personalized Group Recommendation Using Collaborative Filtering and Frequent Pattern 


Vol. 41,  No. 7, pp. 768-774, Jul.  2016


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  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.

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  Cite this article

[IEEE Style]

J. W. Kim and K. Park, "Personalized Group Recommendation Using Collaborative Filtering and Frequent Pattern," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 7, pp. 768-774, 2016. DOI: .

[ACM Style]

Jung Woo Kim and Kwang-Hyun Park. 2016. Personalized Group Recommendation Using Collaborative Filtering and Frequent Pattern. The Journal of Korean Institute of Communications and Information Sciences, 41, 7, (2016), 768-774. DOI: .

[KICS Style]

Jung Woo Kim and Kwang-Hyun Park, "Personalized Group Recommendation Using Collaborative Filtering and Frequent Pattern," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 7, pp. 768-774, 7. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880