收稿日期: 2016-09-21
修回日期: 2016-11-14
网络出版日期: 2017-01-05
基金资助
本文系中央高校基本科研业务费专项资金资助项目“基于社会网络关系的智能专家遴选与推荐平台建设”（项目编号：SKZZB2014037）；教育部人文社会科学研究青年基金项目“面向论文评审专家推荐的兴趣变化挖掘与回避机制生成的研究”（项目编号：16YJC870006）和ISTIC-EBSCO文献大数据发现服务联合实验室基金项目“融合异构科研数据的评审专家推荐研究”研究成果之一。
A Topical Coverage and Authority Unification Model for Expert Recommendation
Received date: 2016-09-21
Revised date: 2016-11-14
Online published: 2017-01-05
[目的/意义] 为投稿论文遴选出合适的审稿专家是论文发表过程中关键的一环。随着投稿论文和候选评审专家数量的持续增长，人工指定评审专家的方法在准确性和公平性上的弊端日益显露出来。因此，为进一步提高专家评审的客观性和准确性，笔者从专家知识与专家权威度两个维度对专家建模，并以此为依据为不同主题的投稿论文遴选推荐评审专家。[方法/过程] 首先分析专家知识以及投稿论文的研究内容，并提取两者涉及的多个子研究主题；然后，计算专家知识对投稿论文子主题的覆盖度，并提出融合主题特征与时间特征的权威度算法TTAM来分析专家权威度；最后，提出融合主题覆盖度和专家权威度的专家推荐框架CAUFER，综合考虑覆盖度和权威度两个因素为投稿论文推荐合适的评审专家。[结果/结论] 实验结果表明，与经典的基于向量空间模型、语言模型和作者主题模型3种专家推荐算法相比，笔者提出的算法能够较好地提高专家与投稿论文的匹配度，并可据此追踪专家权威度的变化，刻画专家在特定主题下的权威度，进一步提高专家推荐的准确性和科学性。
赵千 , 耿骞 , 靳健 , 韦娱 . 一种面向主题覆盖度与权威度的评审专家推荐模型研究[J]. 图书情报工作, 2017 , 61(1) : 80 -88 . DOI: 10.13266/j.issn.0252-3116.2017.01.010
[Purpose/significance] The selection and recommendation of expert play an important role in the process of publications. Due to the remarkable increase in the number of submissions and candidate experts, selecting the proper experts manually for peer reviewer appears its weakness in terms of the accuracy and efficiency. Accordingly, an intelligent algorithm that automatically selects and recommends experts for submissions is of great importance.[Method/process] In this research, the knowledge of each candidate expert and the research content are extracted, which represent several distinct sub-topics. Then, the topical coverage and the authority of reviewer candidates with respect to each submission, which are treated as indispensable evidences for expert recommendation, are deeply exploited. Finally, these two important factors are linearly combined in an integrated model for recommending proper experts, which is named as CAUFER (Coverage and Authority Unification Framework for Expert Recommendation).[Result/conclusion] Different experiments were conducted and the results show that, compared with the Vector Space Model, Language Model and Latent Dirichlet Allocation, the proposed model will effectively grasp the dynamic authority change as well as the different level authority with respect different subtopics.
Key words： expert recommendation; topic coverage; expert authority; authority
[1] HETTICH S, PAZZANI M J. Mining for proposal reviewers:lessons learned at the national science foundation[C]//Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM, 2006:862-871.
[2] BALOG K, AZZOPARDI L, DE RIJKE M. A language modeling framework for expert finding[J]. Information processing & management, 2009, 45(1):1-19.
[3] WANG F, CHEN B, MIAO Z. A survey on reviewer assignment problem[C]//International conference on industrial, engineering and other applications of applied intelligent systems. Berlin:Springer, 2008:718-727.
[4] LIU D R, CHEN Y H, KAO W C, et al. Integrating expert profile, reputation and link analysis for expert finding in question-answering websites[J]. Information processing & management, 2013, 49(1):312-329.
[5] KARIMZADEHGAN M, ZHAI C X. Constrained multi-aspect expertise matching for com-mittee review assignment[C]//Proceedings of the 18th ACM conference on information and knowledge management. New York:ACM, 2009:1697-1700.
[6] ZHANG J, TANG J, LI J. Expert finding in a social network[C]//International conference on database systems for advanced applications. Berlin:Springer, 2007:1066-1069.
[7] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(1):993-1022.
[8] ROSEN-ZVI M, GRIFFITHS T, STEYVERS M, et al. The author-topic model for authors and documents[C]//Proceedings of the 20th conference on uncertainty in artificial intelli-gence.Arlington:AUAI Press, 2004:487-494.
[9] 刘一星,梁山.基于改进ATSVM算法的评审专家自动推荐模型[J].重庆科技学院学报(自然科学版),2010,1:134-136.
[10] MIMNO D,MCCALLU M A. Expertise modeling for matching papers with review-ers[C]//Proceedings of the 13th ACM SIGKDD international conference on knowledge dis-covery and data mining. New York:ACM, 2007:500-509.
[11] 余峰,余正涛,杨剑锋,等.基于主题信息的项目评审专家推荐方法[J].计算机工程,2014,6:201-205.
[12] CHARLIN L, ZEMEL R S. The Toronto paper matching system:an automated paper-reviewer assignment system[C]//International conference on machine learning.Bellevue:AUAI Press,2013,487-494.
[13] MACDONALD C, HANNAH D, OUNIS I. High quality expertise evidence for expert search[C]//European conference on information retrieval. Berlin:Springer, 2008:283-295.
[14] SU H, TANG J, HONG W. Learning to diversify expert finding with subtop-ics[C]//Pacific-Asia conference on knowledge discovery and data mining. Berlin:Springer, 2012:330-341.
[15] KARIMZADEHGAN M, WHITE R W, RICHARDSON M. Enhancing expert finding using organizational hierarchies[C]//European conference on information retrieval. Berlin:Springer, 2009:177-188.
[16] SERDYUKOV P, RODE H, HIEMSTRA D. Modeling expert finding as an absorbing random walk[C]//Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval.New York:ACM, 2008:797-798.
[17] HIRSCH J E. An index to quantify an individual's scientific research output[J]. Proceedings of the national academy of sciences of the united states of america, 2005,102(46):16569-16572.
[18] EGGHE L. Theory and practise of the g-index[J]. Scientometrics, 2006, 69(1):131-152.
[19] KOSMULSKI M. A new hirsch-type index saves time and works equally well as the original h-index[J]. ISSI newsletter, 2006, 2(3):4-6.
[20] 张雪梅. hm 指数——对 h 指数的修正[J]. 图书情报工作, 2007,51(10):116-118.
[21] 叶鹰.一种学术排序新指数——f 指数探析[J]. 情报学报, 2009 (1):142-149.
[22] ALONSO S, CABRERIZO F J, HERRERA-VIEDMA E, et al. hg-index:a new index to characterize the scientific output of researchers based on the h-and g-indices[J]. Scientometrics, 2010, 82(2):391-400.
[23] PAGE L, BRIN S, MOTWANI R, et al. The pagerank citation ranking:bringing order to the web[R]. Palo Alto:Stanford InfoLab,1998:1-14.
[24] KLEINBERG J M. Authoritative sources in a hyperlinked environment[J]. Journal of the ACM, 1999, 46(5):604-632.
[25] HAVELIWALA T H. Topic-sensitive pagerank[C]//Proceedings of the 11th international conference on world wide web. New York:ACM, 2002:517-526.
[26] LIU X, SUEL T, MEMON N. A robust model for paper reviewer assignment[C]//Proceedings of the 8th ACM conference on recommender systems. New York:ACM, 2014:25-32.
[27] ZHOU G, LAI S, LIU K, et al. Topic-sensitive probabilistic model for expert finding in question answer communities[C]//Proceedings of the 21st ACM international conference on Information and knowledge management. New York:ACM, 2012:1662-1666.
[28] SMIRNOVA E, BALOG K. A user-oriented model for expert finding[C]//European confer-ence on information retrieval. Springer Berlin Heidelberg, 2011:580-592.
[29] KOU N M, HOU U L, MAMOULIS N, et al. Weighted coverage based reviewer assign-ment[C]//Proceedings of the 2015 ACM SIGMOD international conference on management of data. New York:ACM, 2015:2031-2046.
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