A Hybrid Collaborative Filtering Algorithm Based on User-Item

Authors:
Yan-ni Chen;Min Yu
Affiliations:
-;-
Venue:
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
Year:
2010

Citing 0
Cited 2

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

Information Processing and Management: an International Journal
TV predictor: personalized program recommendations to be displayed on SmartTVs

Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Collaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative filtering is proposed. This method used a novel similarity measure method to predict the target item rating and it fused the advantages of the user-based algorithm and item-based algorithm with the control factor α. The experimental results show that this improved algorithm obviously enhances the recommended accuracy, and provide better recommendation quality.