Browse > Article
http://dx.doi.org/10.9716/KITS.2014.13.3.309

A Study of Recommendation System Using Association Rule and Weighted Preference  

Moon, Song Chul (남서울대학교 컴퓨터학과)
Cho, Young-Sung (동양미래대학 전산정보학부)
Publication Information
Journal of Information Technology Services / v.13, no.3, 2014 , pp. 309-321 More about this Journal
Abstract
Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
Keywords
Segmentation Method; Asociation Rules; Recommender System;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Agrawal, R. and R Srikant, "Fast Algorithms for Mining Association Rules in Large Databases", In Proceedings of the VLDB, Santiago, Chile, 1994, 487-499.
2 Cho, Y.S., M.S. Gu, and K.H. Ryu, "Development of Personalized Recommendation System using RFM method and k-means Clustering", In : KSCI, Vol.17, No.6, 2012a, 163-171.   과학기술학회마을   DOI   ScienceOn
3 Cho, Y.S., S.C. Moon, S.C. Noh, and K. H. Ryu, "Implementation of Personalized recommendation System using k-means Clustering of Item Category based on RFM", IEEE International Conference on Management of Innovation and Technology Publication, 2012b.
4 Gunaratne, K.A, "Globalisation, Decimation of Labour and Strategic Managerial Options", Twelfth Australia and New Zealand Academy of Management International Conference, 1998.
5 Herlocker, J.L., J.A. Kosran, A. Borchers, and J. Riedl, "An Algorithm Framework for Performing Collaborative Filtering", Proceedings of the 1999 Conference on Research and Development in Information Retrival, 1999.
6 Jung, K.Y., "Personalization Recommendation Using Information Filtering Based on Situational Awareness", NRF-Education, Report of Result, 2008, 31-38.
7 Park, W.B., Y.S. Cho, and H.-H. Ko, "Clustering Method of Weighted Preference Using K-means Algorithm and Bayesian Network for Recommendation System", Journal of Information Technology Application and Management of Korea, Vol.20, No.3, 2013, 219-230.
8 Woo, J.Y., "Segmented CRM strategy via Customer Information Visualization and its Extension to Business Convergence", KAIST Doctoral Dissertation, 2005.