Classification-based collaborative filtering using market basket data

Authors:
Jong-Seok Lee;Chi-Hyuck Jun;Jaewook Lee;Sooyoung Kim
Affiliations:
Department of Industrial Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, South Korea;Department of Industrial Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, South Korea;Department of Industrial Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, South Korea;Department of Industrial Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

Citing 8
Cited 13

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use

CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”

CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Applied multivariate techniques

Applied multivariate techniques
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm

Information Retrieval
Collaborative Filtering Methods for Binary Market Basket Data Analysis

AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology

Knowledge maps for composite e-services: A mining-based system platform coupling with recommendations

Expert Systems with Applications: An International Journal
Analysis and Classification of Multi-Criteria Recommender Systems

World Wide Web
Electronic promotion to new customers using mkNN learning

Information Sciences: an International Journal
An iterative semi-explicit rating method for building collaborative recommender systems

Expert Systems with Applications: An International Journal
User credit-based collaborative filtering

Expert Systems with Applications: An International Journal
Two-way cooperative prediction for collaborative filtering recommendations

Expert Systems with Applications: An International Journal
Improving the Performance of Collaborative Filtering Recommender Systems through User Profile Clustering

WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A personalized recommendation system combining case-based reasoning and user-based collaborative filtering

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Item-based collaborative filtering recommendation using self-organizing map

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A collaborative filtering recommendation algorithm based on SVD smoothing

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A recommender system based on tag and time information for social tagging systems

Expert Systems with Applications: An International Journal
Discriminant analysis of binary data following multivariate Bernoulli distribution

Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

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.