Classification-based collaborative filtering using market basket data
SCIE
SCOPUS
- Title
- Classification-based collaborative filtering using market basket data
- Authors
- Lee, JS; Jun, CH; Lee, J; Kim, S
- Date Issued
- 2005-10
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Abstract
- Collaborative filtering based on voting scores has been known to be the most successful recommendation technique and has been used in a number of different applications. However, since voting scores are not easily available, similar techniques should be needed for the market basket data in the form of binary user-item matrix. We viewed this problem as a two-class classification problem and proposed a new recommendation scheme using binary logistic regression models applied to binary user-item data. We also suggested using principal components as predictor variables in these models. The proposed scheme was illustrated with a numerical experiment, where it was shown to outperform the existing one in terms of recommendation precision in a blind test. (c) 2005 Elsevier Ltd. All rights reserved.
- Keywords
- binary logistic regression; classification; collaborative filtering; market basket data; principal component analysis
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24409
- DOI
- 10.1016/j.eswa.2005.04.037
- ISSN
- 0957-4174
- Article Type
- Article
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, vol. 29, no. 3, page. 700 - 704, 2005-10
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- There are no files associated with this item.
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