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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 823749, 11 pages -

Research ArticleNational University of Defense Technology, Changsha 410073, China

Received 10 April 2014; Accepted 21 August 2014; Published 25 September 2014

Academic Editor: Lu Zhen

Copyright © 2014 Fengjing Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Author: Fengjing Yin, Xiang Zhao, Xin Zhang, Bin Ge, and Weidong Xiao



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