International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 11, Issue 5 (May 2024), Pages: 217-229

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 Original Research Paper

Enhancing research publication choices: A comparative study of journal recommender systems and their effectiveness

 Author(s): 

 Waad Bouaguel 1, *, Nouha Benyounes 2, Chiheb Eddine Ben Ncir 1

 Affiliation(s):

 1MIS Department, College of Business, University of Jeddah, Jeddah, Saudi Arabia
 2LARODEC, ISG, University of Tunis, Tunis, Tunisia

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-2171-0370

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.05.024

 Abstract

In recent years, there has been a rapid increase in the number of research papers being published, leading to what many feel is an overload of information. This makes it difficult for researchers to choose the right journal for their work. To help with this, journal recommender systems have been suggested as useful tools to help researchers find the most appropriate journals for their research. With so many journals, publishers, and recommender systems to choose from, deciding on the best one can be complicated. This decision depends on several factors, including the publisher, the scientific database, and the specific needs and preferences of the user. In this paper, we offer a detailed comparison of popular journal recommender systems, both theoretically and through experiments, to see how effective they are at making recommendations. We focus on how relevant and helpful these recommendations are. We also provide advice for researchers on how to make the most of these recommender systems to aid in their publishing process.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Journal recommender systems, Research publication, Information overload, System effectiveness, Publishing guidance

 Article history

 Received 3 December 2023, Received in revised form 10 April 2024, Accepted 13 May 2024

 Acknowledgment 

This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. UJ-21-DR-63. The authors, therefore, acknowledge with thanks the University of Jeddah for its technical and financial support.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Bouaguel W, Benyounes N, and Ncir CEB (2024). Enhancing research publication choices: A comparative study of journal recommender systems and their effectiveness. International Journal of Advanced and Applied Sciences, 11(5): 217-229

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

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 References (45)

  1. Aymen ATM and Imène S (2022). Scientific paper recommender systems: A review. In: Hatti M (Ed.), Artificial intelligence and heuristics for smart energy efficiency in smart cities: 896-906. Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-92038-8_92   [Google Scholar]
  2. Bai X, Wang M, Lee I, Yang Z, Kong X, and Xia F (2019). Scientific paper recommendation: A survey. IEEE Access, 7: 9324-9339. https://doi.org/10.1109/ACCESS.2018.2890388   [Google Scholar]
  3. Bavdekar SB and Save S (2015). Choosing the right journal for a scientific paper. Journal of the Association of Physicians of India, 63(6): 56-58.   [Google Scholar]
  4. Bergstrom CT, West JD, and Wiseman MA (2008). The eigenfactor™ metrics. Journal of Neuroscience, 28(45): 11433-11434. https://doi.org/10.1523/JNEUROSCI.0003-08.2008   [Google Scholar] PMid:18987179 PMCid:PMC6671297
  5. Curry CL (2019). Journal/author name estimator (JANE). Journal of the Medical Library Association, 107(1): 122-124. https://doi.org/10.5195/jmla.2019.598   [Google Scholar] PMCid:PMC6300233
  6. Elsevier (2022). Measuring a journal’s impact. Available online at: https://www.elsevier.com/researcher/author/tools-and-resources/measuring-a-journals-impact    
  7. Entrup E, Ewerth R, and Hoppe A (2023). A comparison of automated journal recommender systems. In the International Conference on Theory and Practice of Digital Libraries, Springer Nature, Cham, Switzerland: 230-238. https://doi.org/10.1007/978-3-031-43849-3_20   [Google Scholar]
  8. Forrester A, Björk BC, and Tenopir C (2017). New web services that help authors choose journals. Learned Publishing, 30(4): 281-287. https://doi.org/10.1002/leap.1112   [Google Scholar]
  9. Gedikli F (2024). The importance of recommender systems. Available online at: https://medium.com/@Commons/the-importance-of-recommender-systems-36f86f92181   [Google Scholar]
  10. Gupta S and Dave M (2020). An overview of recommendation system: Methods and techniques. In: Sharma H, Govindan K, Poonia R, Kumar S, and El-Medany W (Eds.), Advances in computing and intelligent systems: Algorithms for intelligent systems: 231–237. Springer, Singapore, Singapore. https://doi.org/10.1007/978-981-15-0222-4_20   [Google Scholar]
  11. Herlocker JL, Konstan JA, Borchers A, and Riedl J (1999). An algorithmic framework for performing collaborative filtering. In the Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Berkeley, USA: 230-237. https://doi.org/10.1145/312624.312682   [Google Scholar]
  12. Hu R and Pu P (2009). Acceptance issues of personality-based recommender systems. In the Proceedings of the 3rd ACM Conference On Recommender Systems, Association for Computing Machinery, New York, USA: 221-224. https://doi.org/10.1145/1639714.1639753   [Google Scholar]
  13. Isinkaye FO, Folajimi YO, and Ojokoh BA (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3): 261-273. https://doi.org/10.1016/j.eij.2015.06.005   [Google Scholar]
  14. Jain S, Khangarot H, and Singh S (2019). Journal recommendation system using content-based filtering. In: Kalita J, Balas V, Borah S, and Pradhan R (Eds.), Recent developments in machine learning and data analytics: 99-108. Springer Singapore, Singapore, Singapore. https://doi.org/10.1007/978-981-13-1280-9_9   [Google Scholar]
  15. Jana S (2019). A history and development of peer-review process. Annals of Library and Information Studies, 66(4): 152-162.   [Google Scholar]
  16. Jannach D, Zanker M, Felfernig A, and Friedrich G (2010). Recommender systems: An introduction. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511763113   [Google Scholar]
  17. Kang N, Doornenbal MA, and Schijvenaars RJ (2015). Elsevier journal finder: Recommending journals for your paper. In the Proceedings of the 9th ACM Conference on Recommender Systems, Association for Computing Machinery, Vienna, Austria: 261-264. https://doi.org/10.1145/2792838.2799663   [Google Scholar]
  18. Kim K and Chung Y (2018). Overview of journal metrics. Science Editing, 5(1): 16-20. https://doi.org/10.6087/kcse.112   [Google Scholar]
  19. Konstan JA and Riedl J (2012). Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction, 22: 101-123. https://doi.org/10.1007/s11257-011-9112-x   [Google Scholar]
  20. Kreutz CK and Schenkel R (2022). Scientific paper recommendation systems: A literature review of recent publications. International Journal on Digital Libraries, 23(4): 335-369. https://doi.org/10.1007/s00799-022-00339-w   [Google Scholar] PMid:36212019 PMCid:PMC9533296
  21. Linden G, Smith B, and York J (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1): 76-80. https://doi.org/10.1109/MIC.2003.1167344   [Google Scholar]
  22. Liu H, Yang Z, Lee I, Xu Z, Yu S, and Xia F (2015). CAR: Incorporating filtered citation relations for scientific article recommendation. In the IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), IEEE, Chengdu, China: 513-518. https://doi.org/10.1109/SmartCity.2015.121   [Google Scholar]
  23. Lu J, Wu D, Mao M, Wang W, and Zhang G (2015). Recommender system application developments: A survey. Decision Support Systems, 74: 12-32. https://doi.org/10.1016/j.dss.2015.03.008   [Google Scholar]
  24. Lucas JP, Luz N, Moreno MN, Anacleto R, Figueiredo AA, and Martins C (2013). A hybrid recommendation approach for a tourism system. Expert Systems with Applications, 40(9): 3532-3550. https://doi.org/10.1016/j.eswa.2012.12.061   [Google Scholar]
  25. Martinez L, Rodriguez RM, and Espinilla M (2009). REJA: A georeferenced hybrid recommender system for restaurants. In the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Milan, Italy, 3: 187-190. https://doi.org/10.1109/WI-IAT.2009.259   [Google Scholar]
  26. McNee SM, Albert I, Cosley D, Gopalkrishnan P, Lam SK, Rashid AM, and Riedl J (2002). On the recommending of citations for research papers. In the Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, Association for Computing Machinery, Louisiana, USA: 116-125. https://doi.org/10.1145/587078.587096   [Google Scholar]
  27. Mobasher B, Jin X, and Zhou Y (2003). Semantically enhanced collaborative filtering on the web. In: Berendt B, Hotho A, Mladenič D, van Someren M, Spiliopoulou M, and Stumme G (Eds.), European web mining forum: 57-76. Springer Berlin Heidelberg, Berlin, Germany. https://doi.org/10.1007/978-3-540-30123-3_4   [Google Scholar]
  28. Mudrak B (2015). JournalGuide: Bringing authors and journals together. Learned Publishing, 28(2): 147-149. https://doi.org/10.1087/20150210   [Google Scholar]
  29. Pan C and Li W (2010). Research paper recommendation with topic analysis. In the International Conference on Computer Design and Applications, IEEE, Qinhuangdao, China, 4: V4-264. https://doi.org/10.1109/ICCDA.2010.5541170   [Google Scholar]
  30. Reyna A, Martín C, Chen J, Soler E, and Díaz M (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems, 88: 173-190. https://doi.org/10.1016/j.future.2018.05.046   [Google Scholar]
  31. Rollins J, McCusker M, Carlson J, and Stroll J (2017). Manuscript matcher: A content and bibliometrics-based scholarly journal recommendation system. In the BIR 2017 Workshop on Bibliometric-Enhanced Information Retrieval, Association for Computing Machinery, New York, USA: 18-29.   [Google Scholar]
  32. Schafer JB, Frankowski D, Herlocker J, and Sen S (2007). Collaborative filtering recommender systems. In: Brusilovsky P, Kobsa A, and Nejdl W (Eds.), The adaptive web: Lecture notes in computer science: 291–324.Volume 4321, Springer, Berlin, Germany. https://doi.org/10.1007/978-3-540-72079-9_9   [Google Scholar]
  33. Schafer JB, Konstan JA, and Riedl J (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5: 115-153. https://doi.org/10.1007/978-1-4615-1627-9_6   [Google Scholar]
  34. Shambour Q and Lu J (2015). An effective recommender system by unifying user and item trust information for B2B applications. Journal of Computer and System Sciences, 81(7): 1110-1126. https://doi.org/10.1016/j.jcss.2014.12.029   [Google Scholar]
  35. Singh PK, Pramanik PKD, and Choudhury P (2020). Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends. In: Shrivastava G, Peng SL, Bansal H, Sharma K, and Sharma M (Eds.). New age analytics: Transforming the internet through machine learning, IoT, and trust modeling: 183-215. Apple Academic Press, New Jersey, USA. https://doi.org/10.1201/9781003007210-8   [Google Scholar]
  36. Small H (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4): 265-269. https://doi.org/10.1002/asi.4630240406   [Google Scholar]
  37. Son J and Kim SB (2017). Content-based filtering for recommendation systems using multiattribute networks. Expert Systems with Applications, 89: 404-412. https://doi.org/10.1016/j.eswa.2017.08.008   [Google Scholar]
  38. Song T, Yi C, and Huang J (2017). Whose recommendations do you follow? An investigation of tie strength, shopping stage, and deal scarcity. Information and Management, 54(8): 1072-1083. https://doi.org/10.1016/j.im.2017.03.003   [Google Scholar]
  39. Thorat PB, Goudar RM, and Barve S (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4): 31-36. https://doi.org/10.5120/19308-0760   [Google Scholar]
  40. Tsolakidis A, Triperina E, Sgouropoulou C, and Christidis N (2016). Research publication recommendation system based on a hybrid approach. In the Proceedings of the 20th Pan-Hellenic Conference on Informatics, Association for Computing Machinery, Patras, Greece: 1-6. https://doi.org/10.1145/3003733.3003805   [Google Scholar]
  41. Tung HW and Soo VW (2004). A personalized restaurant recommender agent for mobile e-service. In the IEEE International Conference on e-Technology, e-Commerce and e-Service, IEEE, Taipei, Taiwan: 259-262. https://doi.org/10.1109/EEE.2004.1287319   [Google Scholar]
  42. Van Meteren R and Van Someren M (2000). Using content-based filtering for recommendation. In the Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, 30: 47-56.   [Google Scholar]
  43. Vellino A (2015). Recommending research articles using citation data. Library Hi Tech, 33(4): 597-609. https://doi.org/10.1108/LHT-06-2015-0063   [Google Scholar]
  44. Wang JC and Chiu CC (2008). Recommending trusted online auction sellers using social network analysis. Expert Systems with Applications, 34(3): 1666-1679. https://doi.org/10.1016/j.eswa.2007.01.045   [Google Scholar]
  45. Xia F, Liu H, Lee I, and Cao L (2016). Scientific article recommendation: Exploiting common author relations and historical preferences. IEEE Transactions on Big Data, 2(2): 101-112. https://doi.org/10.1109/TBDATA.2016.2555318   [Google Scholar]