Feasibility Study of Using References Citation Network in Postgraduate Students’ Proposal for Suggesting Related Articles

پژوهشنامه کتابداری و اطلاع رسانی

Article 16, Volume 11, Issue 1 - Serial Number 21, August 2021, Pages 322-334   

Document Type: Research Article

DOI: 10.22067/infosci.2021.24102.0

Authors

Ebrahim Pabarja¹; Javad Abbaspour^{* 2}; Seyed majid Nabavi³

¹Master Grajuated\ knowledge and information science \ Shiraz University, Iran

²َAssistant Prof\knowledge and information science department, Shiraz University, Iran

³Assistant Prof.knowledge and information science department,, Shiraz University, Iran

Abstract

Introduction: The current study aims at determining the possibility of applying the citation network of the available references in postgraduate students’ research proposals for suggesting related articles.
Methodology: As a quantitative and quasi-experimental research, 60 graduate students (30 MA and 30 Ph.D.) from Shiraz University, were purposefully selected and voluntarily participated in the study. The participants were students of different fields of study in the area of social science. The Web of Science Citation Indexing database, from which the source citation network was derived, was used in the study.
Findings: The findings showed that the frequency of the partially related articles was the highest among the three classes of articles (related, partially related, and unrelated). The results of the study also maintained the positive opinion of the users of the related articles suggested by reference citation network to be as effective. Comparing to the MA students, the Ph.D. candidates were also shown to consider the suggested articles to be more effective. That is, the PhD candidates regarded the suggested articles as more related. Moreover, participants with different levels of English language proficiency similarly assessed the relation of the suggested articles.
Conclusion: The findings of this study can provide information for researchers, research systems designers, scientific source and information retrieval system advisors and specialists in the related area about the way the proposals’ citation information can be used for finding relevant information and sources, and accordingly, enriching researches.

Keywords

Citation network; Recommender System; Proposal; Thesis; Related Articles; References

References

 گزانی، سحر (1392). به‌کارگیری روش­های ترکیبی در سیستم­های پیشنهاددهنده برای مقابله با مشکل شروع سرد (مورد مطالعه: سایت پارسی بلاگ). پایان‌نامه کارشناسی ارشد. وزارت علوم، تحقیقات و فناوری‌ـ‌دانشگاه قم‌ـ‌‌دانشکده فنی و مهندسی. بازیابی شده در 12 شهریور 1400 از

 https://www.virascience.com/thesis/author/475635/

مرادی­منش، زهرا؛ درزی، محمد و اصغری، حبیب اله (1388). بررسی و تحلیل فاکتورهای اساسی در طراحی سیستم­های توصیه­گر. دومین کنفرانس بین­المللی شهر الکترونیک. بازیابی شده در

 https://civilica.com/doc/71948.

Agarwal, N., Haque, E., Liu, H., & Parsons, L. (2005). Research paper recommender systems: A subspace clustering approach. In International Conference on Web-Age Information Management, 475-491. https://doi.org/10.1007/11563952_42.

Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., & Xia, F. (2019). Scientific paper recommendation: A survey. IEEE Access, 7, 9324-9339.  doi: 10.1109/ACCESS.2018.2890388.

Beel, J., Aizawa, A., Breitinger, C., & Gipp, B. (2017). Mr. DLib: recommendations-as-a-service (RaaS) for academia. In 2017 ACM/IEEE Joint Conference on Digital Libraries, 1-2. doi: 10.1109/JCDL.2017.7991606.

Hanyurwimfura, D., Bo, L., Havyarimana, V., Njagi, D., & Kagorora, F. (2015). An Effective Academic Research Papers Recommendation for Non-profiled Users. International Journal of Hybrid Information Technology, 8(3), 255-272.‏ DOI: 10.14257/ijhit.2015.8.3.23

Meng, F., Gao, D., Li, W., Sun, X., & Hou, Y. (2013). A unified graph model for personalized query-oriented reference paper recommendation. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 1509-1512. https://doi.org/10.1145/2505515.2507831.

Ollagnier, A., Fournier, S., & Bellot, P. (2018). BIBLME RecSys: Harnessing bibliometric measures for a scholarly paper recommender system. In BIR 2018 workshop on bibliometric-enhanced information retrieval. Retrieved in: https://hal.archives-ouvertes.fr/hal-01770588.

Rollins, J., McCusker, M., Carlson, J., & Stroll, J. (2017). Manuscript Matcher: A Content and Bibliometrics-based Scholarly Journal Recommendation System. In BIR@ ECIR, 18-29.‏Retrived in: https://www.semanticscholar.org/paper/Manuscript-Matcher%3A-A-Content-and-Scholarly-Journal-Rollins- McCusker/ae77408944882af61196021e562ce1cdcee9b4ef.

‏Xia, F., Liu, H., Lee, I., & Cao, L. (2016). Scientific article recommendation: Exploiting common author relations and historical preferences. IEEE Transactions on Big Data, 2(2), 101-112.‏ doi: 10.1109/TBDATA.2016.2555318.