Empirical analysis of the user reputation and clustering property for user-object bipartite networks

User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.

[1]  Marta C. González,et al.  Cycles and clustering in bipartite networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Liu Jian-guo.  Effects of User Tastes on Personalized Recommendation , 2010 .

[3]  Lei Hou,et al.  EMPIRICAL ANALYSIS OF THE CLUSTERING COEFFICIENT IN THE USER-OBJECT BIPARTITE NETWORKS , 2013 .

[4]  An Zeng,et al.  Extracting the Information Backbone in Online System , 2013, PloS one.

[5]  Yan Wang,et al.  A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce , 2015, Inf. Sci..

[6]  Mingsheng Shang,et al.  Group-based ranking method for online rating systems with spamming attacks , 2015, ArXiv.

[7]  Yi-Cheng Zhang,et al.  Effect of user tastes on personalized recommendation , 2009, ArXiv.

[8]  Matús Medo,et al.  The effect of discrete vs. continuous-valued ratings on reputation and ranking systems , 2010, ArXiv.

[9]  Yi-Cheng Zhang,et al.  Empirical analysis of web-based user-object bipartite networks , 2009, ArXiv.

[10]  Yamir Moreno,et al.  Dynamics of rumor spreading in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[12]  Jeffrey Xu Yu,et al.  A topic-biased user reputation model in rating systems , 2015, Knowledge and Information Systems.

[13]  Qiang Guo,et al.  Ranking online quality and reputation via the user activity , 2015 .

[14]  Paul Van Dooren,et al.  Iterative Filtering in Reputation Systems , 2010, SIAM J. Matrix Anal. Appl..

[15]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[16]  H. Herrmann,et al.  New approaches to model and study social networks , 2007, physics/0701107.

[17]  Qiang Guo,et al.  CLUSTERING EFFECT OF USER-OBJECT BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION , 2010 .

[18]  Tao Zhou,et al.  Evaluating user reputation in online rating systems via an iterative group-based ranking method , 2015, ArXiv.

[19]  Giulio Cimini,et al.  Removing spurious interactions in complex networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Qiang Guo,et al.  Information filtering via biased heat conduction , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Yi-Cheng Zhang,et al.  Information filtering via weighted heat conduction algorithm , 2011 .

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

[23]  Yi-Cheng Zhang,et al.  Information filtering via self-consistent refinement , 2008, 0802.3748.

[24]  Yicheng Zhang,et al.  Ranking Reputation and Quality in Online Rating Systems , 2013, PloS one.

[25]  Yi-Cheng Zhang,et al.  Information filtering via Iterative Refinement , 2006, ArXiv.

[26]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[27]  Jianguo Liu,et al.  Identifying online user reputation in terms of user preference , 2018 .

[28]  Sean J. Taylor,et al.  Social Influence Bias: A Randomized Experiment , 2013, Science.

[29]  François Fouss,et al.  A probabilistic reputation model based on transaction ratings , 2010, Inf. Sci..

[30]  Jian-Guo Liu,et al.  Identifying online user reputation of user-object bipartite networks , 2017 .

[31]  Giulio Giunta,et al.  A mathematical model of collaborative reputation systems , 2012, Int. J. Comput. Math..

[32]  Tao Zhou,et al.  A robust ranking algorithm to spamming , 2010, ArXiv.

[33]  Jie Zhang,et al.  A framework for trust modeling in multiagent electronic marketplaces with buying advisors to consider varying seller behavior and the limiting of seller bids , 2013, TIST.