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Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Network Projection
Joint Authors
Yin, Fengjing
Zhao, Xiang
Zhang, Xin
Ge, Bin
Xiao, Weidong
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-25
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Bipartite network projection method has been recently employed for personal recommendation.
It constructs a bipartite network between users and items.
Treating user taste for items as resource in the network, we allocate the resource via links between user nodes and item nodes.
However, the taste model employed by existing algorithms cannot differentiate “dislike” and “unrated” cases implied by user ratings.
Moreover, the distribution of resource is solely based on node degrees, ignoring the different transfer rates of the links.
To enhance the performance, this paper devises a negative-aware and rating-integrated algorithm on top of the baseline algorithm.
It enriches the current user taste model to encompass “like,” “dislike,” and “unrated” information from users.
Furthermore, in the resource distribution stage, we propose to initialize the resource allocation according to user ratings, which also determines the resource transfer rates on links afterward.
Additionally, we also present a scalable implementation in the MapReduce framework by parallelizing the algorithm.
Extensive experiments conducted on real data validate the effectiveness and efficiency of the proposed algorithms.
American Psychological Association (APA)
Yin, Fengjing& Zhao, Xiang& Zhang, Xin& Ge, Bin& Xiao, Weidong. 2014. Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Network Projection. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1046488
Modern Language Association (MLA)
Yin, Fengjing…[et al.]. Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Network Projection. Mathematical Problems in Engineering No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1046488
American Medical Association (AMA)
Yin, Fengjing& Zhao, Xiang& Zhang, Xin& Ge, Bin& Xiao, Weidong. Improving Accuracy and Scalability of Personal Recommendation Based on Bipartite Network Projection. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1046488
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1046488