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


Vol. 13,  No. 7, pp. 1027-1038, Dec.  2006
10.3745/KIPSTD.2006.13.7.1027


PDF
  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 mall. 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.

  Statistics
  Cite this article

[IEEE Style]

J. S. Shim, S. M. Woo, D. H. Lee, Y. S. Kim, S. K. Chung, "Product Recommendation System on VLDB using k-means Clustering and Sequential Pattern Technique," The KIPS Transactions:PartD, vol. 13, no. 7, pp. 1027-1038, 2006. DOI: 10.3745/KIPSTD.2006.13.7.1027.

[ACM Style]

Jang Sup Shim, Seon Mi Woo, Dong Ha Lee, Yong Sung Kim, and Soon Key Chung. 2006. Product Recommendation System on VLDB using k-means Clustering and Sequential Pattern Technique. The KIPS Transactions:PartD, 13, 7, (2006), 1027-1038. DOI: 10.3745/KIPSTD.2006.13.7.1027.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals