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AdaM : adaptive-maximum imputation for neighborhood-based collaborative filtering
conference contribution
posted on 2013-01-01, 00:00 authored by Yongli Ren, Gang LiGang Li, Jun Zhang, Wanlei ZhouIn 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.
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Event
Advances in Social Networks Analysis and Mining. IEEE/ACM International Conference (2013 : Niagara Falls, Ontario)Pagination
628 - 635Publisher
IEEELocation
Niagara, OntarioPlace of publication
Piscataway, N.J.Start date
2013-08-25End date
2013-08-28ISBN-13
9781479914968Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationCopyright notice
2013, IEEE/ACMEditor/Contributor(s)
T Ozyer, P Carrington, E LimTitle of proceedings
ASONAM 2013 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningUsage metrics
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