Paper Info
Title
Collaborative Filtering Based on Star Users
Abstract
As one of the most popular recommender system technologies, neighborhood-based collaborative filtering algorithm has obtained great favor due to its simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. In this paper, we present a novel collaborative filtering method based on user stars. Instead of treating every user as the same, we propose a method to generate a small number of users as the most reliable emph{star users} and then produce predictions for the general population based on star users' ratings. Empirical studies on two different datasets suggest that our method outperforms traditional neighborhood-based collaborative filtering algorithm in terms of both efficiency and accuracy.
Year
DOI
Venue
2011
10.1109/ICTAI.2011.41
ICTAI
Keywords
Field
DocType
neighborhood-based collaborative,user stars,general population,star users,novel collaborative,user star,recommender system,information filtering,recommender systems,collaborative filtering,star user,collaborative filtering method,traditional neighborhood-based collaborative,nearest-neighbor collaborative,different datasets,empirical study,groupware,great favor,predictive models,accuracy,collaboration,prediction model,computational modeling,motion pictures,computer model,nearest neighbor
Recommender system,Small number,Population,Data mining,Collaborative filtering,Computer science,Collaborative software,Artificial intelligence,Machine learning,Empirical research,Recommender systems collaborative filtering
Conference
ISSN
ISBN
Citations 
1082-3409 E-ISBN : 978-0-7695-4596-7
978-0-7695-4596-7
4
PageRank 
References 
Authors
0.55
7
3
Name
Order
Citations
PageRank
Qiang Liu140.55
Bingfei Cheng240.55
Congfu Xu313115.71