Designing a Web-based Journal Recommender System: An Experimental Study on the DOAJ Dataset

Authors

DOI:

https://doi.org/10.17821/srels/2026/v63i3/171908

Keywords:

Content-based Recommender System, Cosine Similarity, DOAJ, Flask, Journal recommendation, Python

Abstract

Researchers generally prefer to publish their manuscripts in a journal related to their subject area. However, they often
struggle to identify suitable journals for manuscript submission. This paper aims to develop a content-based recommender
system to address this problem. This system recommends similar journals based on a known journal title or subject area.
Additionally, it will attempt to design a web-based search interface for the Recommendation System (RS). The DOAJ database
was used for this study. This dataset contains 54 columns as fields, and 16454 journal records approximately. Out of which,
21 pertinent columns were chosen for this study. The Flask web framework for Python was used to display results on
the web. TF-IDF vectorisation with cosine similarity was employed to compute similarity scores between TF-IDF vector
representations of journal metadata.

Downloads

Download data is not yet available.

Downloads

Published

2026-07-31

How to Cite

Nayek, J. K., & Das, R. (2026). Designing a Web-based Journal Recommender System: An Experimental Study on the DOAJ Dataset. Journal of Information and Knowledge, 63(3), 199–206. https://doi.org/10.17821/srels/2026/v63i3/171908

Issue

Volume 63, Issue 3, June 2026

Section

Articles

License

Copyright (c) 2026 Journal of Information and Knowledge

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

All the articles published in Journal of Information and Knowledge are held by the Publisher. Sarada Ranganathan Endowment for Library Science (SRELS), as a publisher requires its authors to transfer the copyright prior to publication. This will permit SRELS to reproduce, publish, distribute and archive the article in print and electronic form and also to defend against any improper use of the article.

Crossref
Scopus
Google Scholar
Europe PMC

References

Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2016). Paper recommender systems: A literature survey. International Journal on Digital Libraries, 17(4), 305-338. https://doi.org/10.1007/s00799-015-0156-0

Jain, S., Khangarot, H., & Singh, S. (2018). Journal recommendation system using content-based filtering. In Recent developments in machine learning and data analytics: IC3 2018 (pp. 99-108). Springer. https://doi. org/10.1007/978-981-13-1280-9_11 PMid:28118817

Lopes, G. R., Souto, M. A. M., Wives, L. K., & de Oliveira, J. P. M. (2008). A personalized recommender system for digital libraries. In Proceedings of the 14th Brazilian Symposium on Multimedia and the Web (pp. 59-66). IEEE. https://doi. org/10.1109/WEBMEDIA.2008.4776134

Ogunde, A. O., Odim, M. O., Olaniyan, O. O., Ojewumi, T. O., Oguntunde, A. O., Fayemiwo, M. A., …, Bolanie, T. H. (2020). The design of a hybrid model-based journal recommendation system. Advances in Science, Technology and Engineering Systems Journal, 5(6), 1153-1162. https:// doi.org/10.25046/aj0506139

Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2011). A literature review and classification of recommender systems in academic journals. Intelligence Information Research, 17(1), 139-152.

Sardar, A., Ferzund, J., Suryani, M. A., & Shoaib, M. (2017). Recommender system for journal articles using opinion mining and semantics. International Journal of Advanced Computer Science and Applications, 8(12), 135-142. https://doi.org/10.14569/IJACSA.2017.081218