A Collaborative Recommend Algorithm Based on Bipartite Community

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database.

[1]  M. Barber.  Modularity and community detection in bipartite networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Roger Guimerà,et al.  Module identification in bipartite and directed networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Jian-Guo Liu,et al.  Improved collaborative filtering algorithm via information transformation , 2007, 0712.3807.

[5]  R. Lambiotte,et al.  Line graphs, link partitions, and overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Huang Chuang.  Uncertain Neighbors' Collaborative Filtering Recommendation Algorithm , 2010 .

[7]  Yu Neng-hai.  Dynamic Reordering Within the Nearest Neighbor-based Algorithm for Collaborative Filtering , 2011 .

[8]  Z. Di,et al.  Clustering coefficient and community structure of bipartite networks , 2007, 0710.0117.

[9]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Neil Genzlinger.  A. and Q , 2006 .

[11]  Johan H. Koskinen,et al.  Modelling the evolution of a bipartite network - Peer referral in interlocking directorates , 2012, Soc. Networks.

[12]  Hsinchun Chen,et al.  Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering , 2004, TOIS.

[13]  Tsuyoshi Murata,et al.  Detecting Communities from Bipartite Networks Based on Bipartite Modularities , 2009, 2009 International Conference on Computational Science and Engineering.

[14]  T. Murata,et al.  Advanced modularity-specialized label propagation algorithm for detecting communities in networks , 2009, 0910.1154.

[15]  Jian Yin,et al.  Uncertain Neighbors'Collaborative Filtering Recommendation Algorithm: Uncertain Neighbors'Collaborative Filtering Recommendation Algorithm , 2010 .

[16]  S. Lehmann,et al.  Biclique communities. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Yuchen Fu,et al.  A Novel Collaborative Filtering Algorithm Based on Bipartite Network Projection , 2012 .

[18]  Yicheng Zhang,et al.  Effects of high-order correlations on personalized recommendations for bipartite networks , 2010 .

[19]  Bin Wu,et al.  Overlapping Community Detection in Bipartite Networks , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[21]  Ken Wakita,et al.  Extracting Multi-facet Community Structure from Bipartite Networks , 2009, 2009 International Conference on Computational Science and Engineering.

[22]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[23]  Yi-Cheng Zhang,et al.  Effect of initial configuration on network-based recommendation , 2007, 0711.2506.

[24]  Fan Ying.  Study on Bipartite Networks , 2010 .

[25]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[26]  Zhu Qing.  Multi-dimensional Adaptive Collaborative Filtering Recommendation Algorithm , 2011 .

[27]  Yi-Cheng Zhang,et al.  Heat conduction process on community networks as a recommendation model. , 2007, Physical review letters.