Optimizing collaborative filtering recommender systems

Authors:
Sung-Hwan Min;Ingoo Han
Affiliations:
Graduate School of Management, Korea Advanced Institute of Science and Technology, Seoul, Korea;Graduate School of Management, Korea Advanced Institute of Science and Technology, Seoul, Korea
Venue:
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Year:
2005

Citing 4
Cited 1

Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Explaining collaborative filtering recommendations

CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Empirical analysis of predictive algorithms for collaborative filtering

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

A comprehensive reference model for personalized recommender systems

HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Collaborative filtering (CF) is the most successful recommendation technique, which has been used in a number of different applications. In traditional CF, the ratings of all items are equally weighted when similarity measure is calculated. But, if the importance of features (or items) is different respectively, feature weighting structure needs to be changed according to the importance of features. This paper presents a GA based feature weighting method. Through this weighting method, we can focus on the good items while removing bad ones or reducing their impacts.