以下成果（截止到2013年10月1日）系“周涛、吕琳媛、张子柯、陈端兵、尚明生”为核心成员，合作完成。 专著数量 1 本，论文总量 94 篇，其中 SCI 论文 76 篇， Google scholar 总引用 3635 ， SCI 论文总影响因子 191.289, 授权专利一项，申请专利 7 项 专利 : （1） 刘臻 , 吕琳媛 , 肖思源 , 刘润然 , 佘莉 . 一种基于无线网络的数据业务推送系统和方法，申请号 :201310168218.3, 申请日： 2013-05-06 （2） 刘臻 , 吕琳媛 , 肖思源 , 刘润然 , 佘莉 . 一种时间窗口的调节方法，申请号 :201310169234.4, 申请日： 2013-05-06 （3） 刘臻 , 吕琳媛 , 肖思源 , 刘润然 , 佘莉 . 根据浏览网页确定用户感兴趣的网页文本的方法和系统，申请号 :201310163619.X, 申请日： 2013-05-06 （4） 刘臻， 吕琳媛 ， 肖思源， 刘润然， 佘莉 . 根据相关网页和当前行为确定用户当前兴趣的方法和系统，申请号 :201310162870.4, 申请日： 2013-05-06 （5） 吕琳媛 , 周艳波 . 一种网络商品个性化推荐方法及系统，申请号 :201310310951.4, 申请日： 2013-07-22 （6） 尚明生 , 傅彦 , 邵刚 , 一种信息推送方法与装置 , 授权号： 2007100874138 ，授权日： 2012-10-17 （7） 尚明生 , 佘莉 , 周涛 , 陈端兵 , 傅彦 , 田军伟 , 一种用户兴趣模型的建立方法 , 申请号： 2009101676383, 申请日： 2009-09-15 （8） 王庆先 , 尚明生 . 一种向客户推荐商品的方法 , 申请号： 2011104483695, 申请日： 2011-12-28 专著： 吕琳媛 ， 周涛 ，链路预测，高等教育出版社， 2013 论文： Q. Ou, Ying-DiJin, T. Zhou , B. –H. Wang, and B. –Q. Yin, Power-law strength-degree correlation fromresource-allocation dynamics on weighted networks, Phys. Rev. E 75 , 021102 (2007). T. Zhou , J. Ren, M. Medo, and Y. –C. Zhang, Bipartite network projection andpersonal recommendation,Phys. Rev.E 76 , 046115 (2007). Y. –C. Zhang,M. Medo, J. Ren, T. Zhou , T. Li, and F. Yang, Recommendation model based on opinion diffusion,EPL 80 , 68003 (2007). T. Zhou , L.-L. Jiang, R.-Q. Su, and Y.-C. Zhang, Effect of initial configurationon network-based recommendation, EPL 81 ,58004 (2008). J. Ren, T. Zhou , and Y.-C. Zhang, InformationFiltering via Self-Consistent Refinement, EPL 82 , 58007 (2008). H.-T. Zhang, M.Z. Q. Chen, G.-B. Stan, T. Zhou , and J. Maciejowski, Collective behavior coordinating with predictivemechanisms, IEEE Circuits andSystems Magazine 2008(3): 67-85 (Feature Article). 汪秉宏， 周涛 ，王文旭，杨会杰，刘建国，赵明，殷传洋，韩筱璞，谢彦波， “ 当前复杂系统研究的几个方向 ” ，复杂系统与复杂性科学 5 (4): 21-28 (2008). R.-R. Liu, C.-X.Jia, T. Zhou , D. Sun, and B.-H. Wang, Personal Recommendation via ModifiedCollaborative Filtering, PhysicaA 388 : 462-468 (2009). 刘建国， 周涛 ，汪秉宏，个性化推荐系统的研究进展， 自然科学进展 19 (1):1-15 (2009). T. Zhou , Personal Recommendation in User-Object Networks, Lecture Notes of the Institute for ComputerSciences, Social-Informatics and Telecommunications Engineering 4 ,247-253 (2009). J.-G. Liu, M. Z. Q.Chen, J. Chen, F. Deng, H.-T. Zhang, Z.-K.Zhang , and T. Zhou , Recent Advances inPersonal Recommender Systems, InternationalJournal of Information and Systems Sciences 5 , 230-247(2009). D. Sun, T. Zhou , J.-G. Liu, R.-R.Liu, C.-X. Jia, and B.-H. Wang, Information filtering based on transferringsimilarity, Phys. Rev. E 80 ,017101 (2009). H.-X. Yang,Z.-X. Wu, C.-S. Zhou, T. Zhou , and B.-H. Wang, Effects of social diversity on the emergence of globalconsensus in opinion dynamics, Phys.Rev. E 80 , 046108 (2009). L. Lü , C.-H. Jin, and T. Zhou , Effective andEfficient Similarity Index for Link Prediction of Complex Networks, Phys. Rev. E 80 , 046122(2009). M. Medo, Y.-C.Zhang, and T. Zhou , Adaptive model for recommendation of news, EPL 88 , 38005 (2009). T. Zhou , R.-Q. Su, R.-R. Liu, L.-L. Jiang, B.-H. Wang, and Y.-C. Zhang, Accurateand diverse recommendations via eliminating redundant correlations, New J.Phys. 11 , 123008 (2009). J.-G. Liu, T. Zhou , B.-H. Wang, Y.-C.Zhang, Q. Guo, Effects of User’s Tastes on Personalized Recommendation, Int. J. Mod. Phys. C 20 ,1925 (2009). 刘建国， 周涛 ，郭强，汪秉宏，个性化推荐系统评价方法综述，复杂系统与复杂性科学， 6 , 1-10(2009) T. Zhou , L. Lü ,and Y.-C. Zhang, Predicting Missing Links via Local Information, Eur. Phys. J.B 71 , 623-630 (2009). M.-S. Shang , and Z.-K.Zhang , Diffusion-Based Recommendation in collaborative Tagging Systems.Chinese Physics Letters 26 ,118903(2009) M.-S. Shang , L.Lü , W. Zeng, Y.-C. Zhang, and T. Zhou , Relevance is More Significant than Correlation: InformationFiltering on Sparse Data, EPL 88 ,68008 (2009). L. Lü , and T. Zhou ,Role of Weak Ties in Link Prediction of Complex Networks, In the proceeding ofthe 18th ACM Conference on Information and Knowledge Management (ACM, New York,2009). L. Lü , C.-H. Jin, T.Zhou , Similarity index based on local paths for link prediction ofcomplex network, Phys. Rev. E 80 ,046122 (2009). M.-S. Shang , C.-H. Jin, T. Zhou , and Y.-C. Zhang, Collaborative filtering based onmulti-channel diffusion, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 388 , 4867-4871(2009) L. Lü , and T. Zhou ,Link Prediction in Weighted Networks: The Role of Weak Ties, EPL 89 , 18001 (2010). L. Lü , Z.-K. Zhang ,and T. Zhou , Zipf’s Law Leadsto Heaps’ Law: Analyzing Their Relation in Finite-Size Systems, PLoS ONE 5 (12), e14139 (2010). M.-S. Shang , L.Lü , Y.-C. Zhang, and T. Zhou ,Empirical analysis of web-based user-object bipartite networks, EPL 90 , 48006 (2010). W. Zeng, M.-S.Shang , Q.-M. Zhang, L. Lü ,and T. Zhou , Can DissimilarUsers Contribute to Accuracy and Diversity of Personalized Recommendation? Int.J. Mod. Phys. C 21 , 1217 (2010). L. Lü , J.-A. Lu, Z.-K.Zhang , X.-Y. Yan, Y. Wu, D.-H. Shi, H.-P. Zhou, J.-Q. Fang, and T. Zhou , Looking into ComplexNetworks, Complex Systems and Complexity Science 7(2-3), 173 (2010). J.-G.Liu, T. Zhou , H.-A. Che, B.-H. Wang, and Y.-C. Zhang, Effects of high-ordercorrelations on personalized recommendations for bipartite networks, Physica A 389 881(2010). M.-S. Shang , G.-X. Chen, S.-X. Dai, B.-H. Wang, and T. Zhou , Interest-Driven Model for Human Dynamics, Chin. Phys. Lett. 27 , 048701 (2010). J.-G. Liu, T. Zhou , B.-H. Wang, Y.-C.Zhang, and Q. Guo, Degree Correlation of Bipartite Network on PersonalizedRecommendation, Int. J. Mod.Phys. C 21 , 137-147 (2010). T. Zhou , Z. Kuscsik, J.-G.Liu, M. Medo, J. R. Wakeling, and Y.-C. Zhang, Solving the apparent diversity-accuracydilemma of recommender systems, PNAS 107 ,4511-4515 (2010) H.-T. Zhang, N.Wang, M. Z. Q. Chen, R.-Q. Su, T. Zhou , and C. Zhou, Spatially quantifying the leadership effectiveness incollective behaviors, New J.Phys. 12 , 123025 (2010). 汪秉宏， 周涛 ，刘建国，推荐系统、信息挖掘及基于互联网的信息物理研究，复杂系统与复杂性科学， 7 , 46-49(2010) W.-P. Liu, and L. Lü , Link Prediction based on Local Random walk, EPL 89 , 58007 (2010). M.-S. Shang , Z.-K.Zhang , T. Zhou , and Y.-C.Zhang, Collaborative filtering with diffusion-based similarity fusion ontripartite graphs. Physica A 389 ,1259-1264(2010) Q.-M. Zhang, M.-S. Shang , and L.Lü , Similarity-Based Classification in Partially Labeled Networks, Int.J. Mod. Phys. C 21 , 813 (2010) Q.-M. Zhang, M.-S. Shang , W. Zeng, Y. Chen, and L. Lü , Empirical comparison of local structural similarityindices for collaborative-filtering-based recommender systems, Physics Procedia 3 , 1887 (2010). Z.-K. Zhang , T.Zhou , and Y.-C. Zhang, Personalized Recommendation via IntegratedDiffusion on User-Item-Tag Tripartite Graphs. Physica A, 389 , 179-186 (2010) Z.-K. Zhang , C. Liu, Y.-C. Zhang, and T. Zhou, Solving the Cold-StartProblem in Recommender Systems with Social Tags. EPL 92 28002 (2010) P. Wu, and Z.-K. Zhang . Enhancing personalized recommendation inweighted social tagging networks. Physical Procdia 3, 1877-1885(2010) L. Lü , Link Prediction on Complex Networks (in Chinese),Journal of University of Electronic Science and Technology of China 39 (5), 651 (2010). L. Lü , Y.-C. Zhang, C. H. Yeung, and T. Zhou , Leaders in Social Networks, the delicious case,PLoS ONE 6 (6): e21202 (2011). L. Lü , D.-B. Chen ,and T. Zhou , Small worldyields the most effective information spreading, New J. Phys. 13 , 123005 (2011). Z. Liu, Q.-M. Zhang, L. Lü , and T. Zhou ,Link prediction in complex networks: a local naïve Bayes model, EPL 96 , 48007 (2011). W. Zeng, Y.-X. Zhu, L. Lü , and T. Zhou ,Negative ratings play a positive role in information filtering, Physica A 390 , 4486-4493 (2011) H.-K. Liu, L. Lü , and T. Zhou ,Uncovering the network evolution mechanism by link prediction, Sci Sin PhysMech Astron 41 , 816-823 (2011) G. Cimini, M.Medo, T. Zhou , D. Wei, and Y.-C. Zhang, Heterogeneity, quality, and reputation in an adaptiverecommendation model, Eur. Phys.J. B 80 , 201-208 (2011). D. Wei, T. Zhou , G. Cimini, P. Wu,W. Liu, and Y.-C. Zhang, Effective mechanism for social recommendation ofnews, Physica A 390 ,2117-2126 (2011) Y.-B. Zhou, T.Lei, T. Zhou , A robust ranking algorithm to spamming, EPL 94 , 48002 (2011). T. Zhou , M. Medo, G. Cimini, Z.-K. Zhang , and Y.-C. Zhang, Emergence of Scale-Free Leadership Structure in SocialRecommender Systems, PLoS ONE 6 ,e20648 (2011). J.-G. Liu, T. Zhou , and Q. Guo, Informationfiltering via biased heat conduction, Phys. Rev. E 84 , 037101 (2011). T. Qiu, G. Chen, Z.-K. Zhang , and T. Zhou , An item-oriented recommendation algorithm on cold-start problem, EPL 95 , 58003 (2011). L. Lü , and W. Liu, Informationfiltering via preferential diffusion, Phys. Rev. E 83 , 066119 (2011) . Z.-K. Zhang , and C. Liu. Identifying the Role of SocialTags and its Application in Recommender Systems. International Journal of Complex Systems inScience, 1 10 (2011) L. Lü, and T. Zhou ,Link prediction in complex networks: A survey, Physica A 390 , 1150 (2011). Z.-K. Zhan g, T.Zhou , and Y.-C. Zhang, Tag-Aware Recommender systems: Astate-of-the-art survey. Journal of Computer Science and Technology 26 , 767-777 (2011). T. Qiu, G. Chen, Z.-K. Zhang , and T.Zhou , An Item oriented recommendation algorithm on cold start problem,EPL 95 58003 (2011). L. Lü , M. Medo, C. H. Yeung, Y.-C. Zhang, Z.-K. Zhang , and T.Zhou , Recommender Systems, Physics Reports 519 , 1-49 (2012). D.-B. Chen , L.Lü , M.-S. Shang ,Y.-C. Zhang, and T. Zhou ,Identifying influential nodes in complex networks, Physica A 391 , 1777-1787 (2012). Y.-X. Zhu, L. Lü , Q.-M. Zhang, and T.Zhou , Uncovering missing links with cold ends, Physica A 391 , 5769-5778 (2012). Z. Yang, Z.-K.Zhang , and T. Zhou ,Anchoring bias in online voting, EPL 100 ,68002 (2012). H. Liu, F. Yu, A. Zeng, and L. Lü , Recommendation of leadersin online social systems, ISMIS’12 Lecture Notes in Artificial Intelligence 7661 , 387-396 (2012). Y.-B. Zhou, L. Lü , and M. Li, Quantifying the influence of scientistsand their publications: distinguishing between prestige and popularity, New J.Phys. 14 , 033033 (2012). Y.-X. Zhu, and L. Lü , Evaluation Metrics for Recommender Systems, Journalof University of Electronic Science and Technology of China 41 , 163-175 (2012). A. Zeng, L.Lü , T. Zhou ,Manipulating directed networks for better synchronization, New J. Phys. 14 , 083006 (2012). Z.-K. Zhang , and C. Liu, Hybrid Recommendation Algorithmbased on two roles of social tags, International Journal of Bifurcation andChaos 22 , 1250166 (2012). G. Cimini, D.-B. Chen , L. Lü , M. Medo, Y.-C. Zhang, and T. Zhou, Enhancing topologyadaptation in information-sharing social networks, Physical Review E 85 , 046108(2012) J. Huang, X.-Q.Cheng, H.-W. Shen, T. Zhou , and X. Jin, Exploring social influence via posterior effect ofword-of-mouth recommendations, WSDM'12, ACM Press, 2012, pages 573-582. 荣智海，唐明，汪小帆，吴枝喜，严钢， 周涛 ，复杂网络 2012 年度盘点，电子科技大学学报 41, 801-807 (2012) A. Zeng, C.-H. Yeung, M.-S. Shang , and Y.-C. Zhang, The reinforcing influence ofrecommendations on global diversification, EPL 97 , 18005(2012) 张子柯 , 社会化标签系统的结构、演化和功能。上海理工大学学报 32 , 444-451(2012) D.-B. Chen , and H. Gao, An Improved Adaptive model onInformation of Recommending and Spreading, Chinese Physics Letters 29, 048901(2012) D.-B. Chen , H Gao, L.Lü* , and T. Zhou, Identifying influential nodes in large-scale directed networks: The role ofclustering, PLoS ONE 8 , e77455(2013). L. Lü , Z.-K. Zhang ,and T. Zhou , Deviation ofZipf’s and Heaps’ Laws in Human Languages with Limited Dictionary Sizes, ScientificReports 3 , 1082 (2013). Q.-M. Zhang, L. Lü , W.-Q. Wang, Y.-X. Zhu, and T. Zhou , Potential Theory for Directed Networks, PLoS ONE 8 (2), e55437 (2013). Y. Zhou, L.Lü ， W. Liu, and J. Zhang, The Power of Ground User inRecommender Systems ， PLoS ONE 8 ,e70094 (2013). F. Guo, Z.Yang, and T. Zhou , Predicting link directions via arecursive subgraph-based ranking, Physica A 392, 3402–3408 (2013). Z.-D. Zhao, Z. Yang, Z.-K. Zhang , T. Zhou , Z.-G. Huang, and Y.-C. Lai, Emergence ofscaling in human-interest dynamics, Scientific Reports 3 , 3472 (2013). M. Zheng, L. Lü , and M. Zhao ， Spreading in online social networks: The role of socialreinforcement, Phys. Rev. E 88 ,012818 (2013) Z.-K. Zhang, Y. Sun, C.-X. Zhang, K. Fang, X. Xu, C. Liu,X. Wang, and K. Zhang. Diagnosing and Predicting the Earth's Health viaEcological Network Analysis. 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Shang , Extracting theInformation Backbone in Online System , PLoS ONE 8, e62624(2013) Y.-W. Dong, S.-M. Cai, and M.-S. Shang , Empirical study onscaling of human behaviors in e-commerce, ACTA PHYSICA SINICA 62 , 028901(2013) G. Cimini, A. Zeng, M. Medo, and D.-B. Chen , The role of tasteaffinity in agent-based model for social recommendation, Advances in ComplexSystems, 1350009(2013) D.-B. Chen , A. Zeng, G. Cimini, and Y.-C. Zhang,Adaptive social recommendation in a multiple category landscape, Eur. Phys. J.B 86 , 61(2013) 王冠楠 , 陈端兵 , 傅彦 , 新闻推荐的多维兴趣模型与传播分析 . 计算机科学 40 , 126-130(2013) 王军， 张子柯 ，基于社会化标签信息熵的个性化推荐算法 , 图书情报工作 57 , 31-35(2013)
Negative ratings play a positive role in information filtering The explosive growth of information asks for advanced information filtering techniques to solve the so-called information overload problem. A promising way is the recommender system which analyzes the historical records of users’ activities and accordingly provides personalized recommendations. Most recommender systems can be represented by userobject bipartite networks where users can evaluate and vote for objects, and ratings such as ‘‘dislike’’ and ‘‘I hate it’’ are treated straightforwardly as negative factors or are completely ignored in traditional approaches. Applying a local diffusion algorithm on three benchmark data sets, MovieLens, Netflix and Amazon, our study arrives at a very surprising result, namely the negative ratings may play a positive role especially for very sparse data sets. In-depth analysis at the microscopic level indicates that the negative ratings from less active users to less popular objects could probably have positive impacts on the recommendations, while the ones connecting active users and popular objects mostly should be treated negatively. We finally outline the significant relevance of our results to the two long-term challenges in information filtering: the sparsity problem and the coldstart problem. Author： Wei Zeng, Yu-Xiao Zhu, Linyuan Lü, Tao Zhou Journal ：Physica A 390 (2011) 4486–4493. Download ： Negative ratings play a positive role in information filtering_PHYSA13305.pdf
标签: 信息过滤
