Skip to main content
SpringerLink
Log in

Recommending Reviewers and Experts

  • Chapter
  • First Online:
Knowledge Recommendation Systems with Machine Intelligence Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1101))

  • 71 Accesses

Abstract

This chapter focuses on the recommendation of reviewers and experts for the evaluation of scholarly articles and research and development projects. Its main objective is to propose a recommendation system of reviewers and experts—specifically, its architecture and recommendation algorithm, by presenting case studies concerning the architecture and functions of each of its modules.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Apache Lucene–Similarity Measure–http://lucene.apache.org.

  2. 2.

    http://cordis.europa.eu/projects/home_en.html, 2015-11-18.

  3. 3.

    http://recenzenci.opi.org.pl.

  4. 4.

    https://lucene.apache.org.

References

  1. August D, Muraskin L (1999) Strengthening the standards: Recommendations for oeri peer review. In: Summary report. Prepared for the national educational research policy and priorities board, US Department of Education

    Google Scholar 

  2. Basu C, Hirsh H, Cohen WW (2001) Technical paper recommendation: A study in combining multiple information sources. J Artif Intell Res 14:231–252

    Article  MATH  Google Scholar 

  3. Bornmann L, Daniel HD (2009) Reviewer and editor biases in journal peer review: an investigation of manuscript refereeing at angewandte chemie international edition. Res Eval 18(4):262–272

    Google Scholar 

  4. Eisenhart M (2002) The paradox of peer review: Admitting too much or allowing too little? Res Sci Educ 32(2):241–255

    Article  Google Scholar 

  5. Flach PA, Spiegler S, Golénia B, Price S, Guiver J, Herbrich R, Graepel T, Zaki MJ (2010) Novel tools to streamline the conference review process: Experiences from sigkdd’09. ACM SIGKDD Explor Newsl 11(2):63–67

    Article  Google Scholar 

  6. Hemlin S (2009) Peer review agreement or peer review disagreement: Which is better? J Psychol Sci Technol 2(1):5–12

    Article  Google Scholar 

  7. Hoang DT, Nguyen NT, Collins B, Hwang D (1999) Decision support system for solving reviewer assignment problem. 52(5):379–397

    Google Scholar 

  8. Hojat M, Rosenzweig S (2004) Journal peer review in integrative medicine discipline. Sem Integr Med 2(1):1–4

    Google Scholar 

  9. Jacoby LL, Kelley C, Brown J, Jasechko J (1989) Becoming famous overnight: Limits on the ability to avoid unconscious influences of the past. J Pers Soc Psychol 56(3):326–338

    Article  Google Scholar 

  10. Langfeldt L (2004) Expert panels evaluating research: decision-making and sources of bias. Res Eval 13(1):51–62

    Article  Google Scholar 

  11. Liu P, Dew P (2004) Using semantic web technologies to improve expertise matching within academia. In: Proceedings of I-KNOW, Graz, Austria, pp 70–378

    Google Scholar 

  12. Liu X, Wang X, Zhu D (1999) Reviewer recommendation method for scientific research proposals: a case for NSFC. 127(6):3343–3366

    Google Scholar 

  13. Mabude CN, Awoyelu IO, Akinyemi BO, Aderounmu GA (1999) An integrated approach to research paper and expertise recommendation in academic research. 13(4):485–495

    Google Scholar 

  14. Marsh HW, Jayasinghe UW, Bond NW (2008) Improving the peer-review process for grant applications: Reliability, validity, bias, and generalizability. Am Psychol 63(3):160–168

    Article  Google Scholar 

  15. Mishra D, Singh SK (2011) Taxonomy-based discovery of experts and collaboration networks. VSRD Int J Comput Sci Inf Technol I(10):698–710

    Google Scholar 

  16. Papagelis M, Plexousakis D, Nikolaou PN (2005) CONFIOUS: Managing the electronic submission and reviewing process of scientific conferences. In: Web information systems engineering-WISE 2005, vol 3806. Lecture Notes in Computer Science. Springer, Berlin, pp 711–720

    Google Scholar 

  17. Pradhan T, Sahoo S, Singh U, Pal S (1999) A proactive decision support system for reviewer recommendation in academia. 169

    Google Scholar 

  18. Protasiewicz J (2014) A support system for selection of reviewers. In: Systems, man and cybernetics (SMC), 2014 IEEE International Conference on. IEEE, pp 3062–3065

    Google Scholar 

  19. Protasiewicz J, Artysiewicz J, Dadas S, Gałężewska M, Kozłowski M, Kopacz A, Stanisławek T (2012) Procedury recenzowania i doboru recenzentów. Tom 2, vol 2. OPI PIB

    Google Scholar 

  20. Protasiewicz J, Dadas S, Gałężewska M, Kłodziński P, Kopacz A, Kotynia M, Langa M, Młodożeniec M, Oborzyński A, Stanisławek T, Stańczyk A, Wieczorek A (2012) Procedury recenzowania i doboru recenzentów. Tom 1, vol 1. OPI PIB

    Google Scholar 

  21. Protasiewicz J, Pedrycz W, Kozłowski M, Dadas S, Stanisławek T, Kopacz A, Gałężewska M (2016) A recommender system of reviewers and experts in reviewing problems. Knowl Based Syst 106:164–178

    Article  Google Scholar 

  22. Rivara FP, Cummings P, Ringold S, Bergman AB, Joffe A, Christakis DA (2007) A comparison of reviewers selected by editors and reviewers suggested by authors. J Pediatr 151(2):202–205

    Google Scholar 

  23. Ryabokon A, Polleres A, Friedrich G, Falkner AA, Haselböck A, Schreiner H (2012) (re) configuration using web data: A case study on the reviewer assignment problem. In: International conference on web reasoning and rule systems. Springer, pp 258–261

    Google Scholar 

  24. Spier R (2002) The history of the peer-review process. Trends Biotechnol 20(8):357–358

    Article  Google Scholar 

  25. Tian Q, Ma J, Liang J, Kwok RC, Liu O (2005) An organizational decision support system for effective & project selection. Decis Support Syst 39(3):403–413

    Google Scholar 

  26. Tian Q, Maa J, Liua O (2002) A hybrid knowledge and model system for R &D project selection. Expert Syst Appl 39(3):265–271

    Article  Google Scholar 

  27. Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131

    Article  Google Scholar 

  28. Xu Y, Ma J, Sun Y, Hao G, Xu W, Zhao D (2010) A decision support approach for assigning reviewers to proposals. Expert Syst Appl 37(10):6948–6956

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Information Processing Institute, Warsaw, Poland

    Jarosław Protasiewicz

Authors
  1. Jarosław Protasiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jarosław Protasiewicz .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Protasiewicz, J. (2023). Recommending Reviewers and Experts. In: Knowledge Recommendation Systems with Machine Intelligence Algorithms. Studies in Computational Intelligence, vol 1101. Springer, Cham. https://doi.org/10.1007/978-3-031-32696-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation