AdaM : adaptive-maximum imputation for neighborhood-based collaborative filtering

Ren, Yongli, Li, Gang, Zhang, Jun and Zhou, Wanlei 2013, AdaM : adaptive-maximum imputation for neighborhood-based collaborative filtering, in ASONAM 2013 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, Piscataway, N.J., pp. 628-635.

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Title AdaM : adaptive-maximum imputation for neighborhood-based collaborative filtering
Author(s) Ren, Yongli
Li, Gang
Zhang, Jun
Zhou, Wanlei
Conference name Advances in Social Networks Analysis and Mining. IEEE/ACM International Conference (2013 : Niagara Falls, Ontario)
Conference location Niagara, Ontario
Conference dates 25-28 Aug. 2013
Title of proceedings ASONAM 2013 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Editor(s) Ozyer, Tansel
Carrington, Peter
Lim, Ee-Peng
Publication date 2013
Conference series IEEE/ACM International Conference Advances in Social Networks Analysis and Mining
Start page 628
End page 635
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary In the context of collaborative filtering, the well known data sparsity issue makes two like-minded users have little similarity, and consequently renders the k nearest neighbour rule inapplicable. In this paper, we address the data sparsity problem in the neighbourhood-based CF methods by proposing an Adaptive-Maximum imputation method (AdaM). The basic idea is to identify an imputation area that can maximize the imputation benefit for recommendation purposes, while minimizing the imputation error brought in. To achieve the maximum imputation benefit, the imputation area is determined from both the user and the item perspectives; to minimize the imputation error, there is at least one real rating preserved for each item in the identified imputation area. A theoretical analysis is provided to prove that the proposed imputation method outperforms the conventional neighbourhood-based CF methods through more accurate neighbour identification. Experiment results on benchmark datasets show that the proposed method significantly outperforms the other related state-of-the-art imputation-based methods in terms of accuracy.
ISBN 9781479914968
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE/ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057134

Document type: Conference Paper
Collection: School of Information Technology
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