Opinion leader based filtering

Authors:
Hyeonjae Cheon;Hongchul Lee
Affiliations:
Department of Industrial Systems and Information Engineering, Korea University, Seoul, South Korea;Department of Industrial Systems and Information Engineering, Korea University, Seoul, South Korea
Venue:
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Year:
2005

Citing 8
Cited 1

GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation

Communications of the ACM
Analysis of recommendation algorithms for e-commerce

Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
Industrial Applications of Fuzzy Control

Industrial Applications of Fuzzy Control
Modern Information Retrieval

Modern Information Retrieval
Dynamic Expert Group Models for Recommender Systems

WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments

UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence

An improved mix framework for opinion leader identification in online learning communities

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recommendation systems are helping users find the information, products, and other people they most want to find, therefore many on-line stores provide recommending services e.g. Amazon, CDNOW, etc. Most recommendation systems use collaborative filtering, content-based filtering, and hybrid techniques to predict user preferences. We discuss the strengths and weaknesses of the techniques and present a unique recommendation system that automatically selects opinion leaders by category or genre to improve the performance of recommendation. Finally, our approach will help to solve the cold-start problem in collaborative filtering.