DocumentCode :
2802497
Title :
An Efficient Collaborative Filtering Approach Using Smoothing and Fusing
Author :
Zhang, Daqiang ; Cao, Jiannong ; Zhou, Jingyu ; Guo, Minyi ; Raychoudhury, Vaskar
Author_Institution :
Dept. of Comput. Sci., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2009
fDate :
22-25 Sept. 2009
Firstpage :
558
Lastpage :
565
Abstract :
Collaborative filtering (CF) has achieved widespread success in recommender systems such as Amazon and Yahoo! music. However, CF usually suffers from two fundamental problems - data sparsity and limited scalability. Among the two broad classes of CF approaches, namely, memory-based and model-based, the former usually falls short of the system scalability demands, because these approaches predict user preferences over the entire item-user matrix. The latter often achieves unsatisfactory accuracy, because they cannot capture precisely the diversity in user rating styles. In this paper, we propose an efficient collaborative filtering approach using smoothing and fusing (CFSF) strategies. CFSF formulates the CF problem as a local prediction problem by mapping it from the entire large-scale item-user matrix to a locally reduced item-user matrix. Given an active item and a user, CFSF dynamically constructs a local item-user matrix as the basis of prediction. To alleviate data sparsity, CFSF presents a fusion strategy for the local item-user matrix that fuses ratings of the same user makes on similar items, and ratings of like-minded users make on the same and similar items. To eliminate diversity in user rating styles, CFSF uses a smoothing strategy that clusters users over the entire item-user matrix and then smoothes ratings within each user cluster. Empirical study shows that CFSF outperforms the state-of-the-art CF approaches in terms of both accuracy and scalability.
Keywords :
groupware; information filtering; matrix algebra; recommender systems; collaborative filtering; data sparsity; fusing; item-user matrix; recommender systems; smoothing; Computer science; Concurrent computing; Filtering; International collaboration; Large-scale systems; Parallel processing; Predictive models; Recommender systems; Scalability; Smoothing methods; Collaborative Filtering; Data Sparsity; Fusing; Limited Scalability; Recommender Systems; Smoothing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel Processing, 2009. ICPP '09. International Conference on
Conference_Location :
Vienna
ISSN :
0190-3918
Print_ISBN :
978-1-4244-4961-3
Electronic_ISBN :
0190-3918
Type :
conf
DOI :
10.1109/ICPP.2009.16
Filename :
5362485
Link To Document :
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