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http://dx.doi.org/10.3745/KIPSTD.2006.13D.7.1027

Product Recommendation System on VLDB using k-means Clustering and Sequential Pattern Technique  

Shim, Jang-Sup (정보통신연구진흥원 정보화추진팀)
Woo, Seon-Mi (전북대학교 전자정보공학부)
Lee, Dong-Ha ((주)넷스루 데이터 마이닝 연구소)
Kim, Yong-Sung (전북대학교 전자정보공학부)
Chung, Soon-Key (전북대학교 전기전자 컴퓨터공학부)
Publication Information
The KIPS Transactions:PartD / v.13D, no.7, 2006 , pp. 1027-1038 More about this Journal
Abstract
There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.
Keywords
Product Recommendation System; K-means Clustering; Sequence Pattern;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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