A Collaborative Filtering Algorithm Employing Genetic Clustering to Ameliorate the Scalability Issue

Authors:
Feng Zhang;Hui-you Chang
Affiliations:
University,Guangzhou,510275,China;University,Guangzhou,510275,China
Venue:
ICEBE '06 Proceedings of the IEEE International Conference on e-Business Engineering
Year:
2006

Citing 0
Cited 4

An improved privacy-preserving DWT-based collaborative filtering scheme

Expert Systems with Applications: An International Journal
A comparison of clustering-based privacy-preserving collaborative filtering schemes

Applied Soft Computing
Recommender systems survey

Knowledge-Based Systems
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering

Information Processing and Management: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Collaborative filtering technologies are facing two major challenges: scalability and recommendation quality, which are two goals in conflict. Nowadays more studies are focusing on the quality issue but less on the scalability one. We introduce a genetic clustering algorithm to partition the source data, guaranteeing that the intra-similarity will be high but the inter-similarity will be low. The clustering process is off-line running. Our empirical results show that the genetic clustering based collaborative filtering recommender system outperforms the memory-based one in scalability, and outperforms the k-means clustering based one and the memory-based one in recommendation quality.