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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
[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
Zhao Qian , Geng Qian , Jin Jian , Wei Yu . A Topical Coverage and Authority Unification Model for Expert Recommendation[J]. Library and Information Service, 2017 , 61(1) : 80 -88 . DOI: 10.13266/j.issn.0252-3116.2017.01.010
[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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