A User-Item Predictive Model for Collaborative Filtering Recommendation

Authors:
Heung-Nam Kim;Ae-Ttie Ji;Cheol Yeon;Geun-Sik Jo
Affiliations:
Intelligent E-Commerce Systems Lab., Dept. of Information Engineering, Inha University,;Intelligent E-Commerce Systems Lab., Dept. of Information Engineering, Inha University,;Intelligent E-Commerce Systems Lab., Dept. of Information Engineering, Inha University,;School of Information Engineering, Inha University, Incheon, Korea
Venue:
UM '07 Proceedings of the 11th international conference on User Modeling
Year:
2007

Citing 5
Cited 0

GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
Item-based top-N recommendation algorithms

ACM Transactions on Information Systems (TOIS)
Enhanced prediction algorithm for item-based collaborative filtering recommendation

EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
A user-item relevance model for log-based collaborative filtering

ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Collaborative Filtering recommender systems, one of the most representative systems for personalized recommendations in E-commerce, enable users to find the useful information easily. But traditional CF suffers from some weaknesses: scalability and real-time performance. To address these issues, we present a novel model-based CF approach to provide efficient recommendations. In addition, we propose a new method of building a model with dynamic updates, when users present explicit feedback. The experimental evaluation on MovieLensdatasets shows that our method offers reasonable prediction quality as good as the best of user-based Pearson correlation coefficient algorithm.