You are not logged in.

The efficient imputation method for neighborhood-based collaborative filtering

Ren, Yongli, Li, Gang, Zhang, Jun and Zhou, Wanlei 2012, The efficient imputation method for neighborhood-based collaborative filtering, in CIKM 2012 : Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, New York, N.Y., pp. 684-693, doi: 10.1145/2396761.2396849.

Attached Files
Name Description MIMEType Size Downloads

Title The efficient imputation method for neighborhood-based collaborative filtering
Author(s) Ren, Yongli
Li, GangORCID iD for Li, Gang
Zhang, JunORCID iD for Zhang, Jun
Zhou, WanleiORCID iD for Zhou, Wanlei
Conference name Information and Knowledge Management. Conference (21st : 2012 : Maui, Hawaii)
Conference location Maui, Hawaii
Conference dates 29 Oct. -2 Nov.2012
Title of proceedings CIKM 2012 : Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Editor(s) [Unknown]
Publication date 2012
Conference series Information and Knowledge Management Conference
Start page 684
End page 693
Total pages 10
Publisher ACM
Place of publication New York, N.Y.
Keyword(s) collaborative filtering
recommender systems
Summary As each user tends to rate a small proportion of available items, the resulted Data Sparsity issue brings significant challenges to the research of recommender systems. This issue becomes even more severe for neighborhood-based collaborative filtering methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the Data Sparsity issue in the context of the neighborhood-based collaborative filtering. Given the (user, item) query, a set of key ratings are identified, and an auto-adaptive imputation method is proposed to fill the missing values in the set of key ratings. The proposed method can be used with any similarity metrics, such as the Pearson Correlation Coefficient and Cosine-based similarity, and it is theoretically guaranteed to outperform the neighborhood-based collaborative filtering approaches. Results from experiments prove that the proposed method could significantly improve the accuracy of recommendations for neighborhood-based Collaborative Filtering algorithms. © 2012 ACM.
ISBN 9781450311564
Language eng
DOI 10.1145/2396761.2396849
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 14 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 316 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Mon, 18 Mar 2013, 09:41:57 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact