Please use this identifier to cite or link to this item:
Title: An efficient collaborative filtering approach using smoothing and fusing
Authors: Zhang, D
Cao, J 
Zhou, J
Guo, M
Raychoudhury, V
Issue Date: 2009
Source: Proceedings of the International Conference on Parallel Processing, 2009, 5362485, p. 558-565 How to cite?
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 likeminded 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.
Description: 38th International Conference on Parallel Processing, ICPP-2009, Vienna, 22-25 September 2009
ISBN: 9780769538020
ISSN: 0190-3918
DOI: 10.1109/ICPP.2009.16
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 13, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.