Mapping the research landscape of recommender systems for digital libraries
A bibliometric analysis of two decades (2004-2023)
Downloads
Background of the study: In today's information-rich environment, researchers face the difficulty of managing information overload and struggle to identify meaningful information among a plethora of choices. A recommender system plays a crucial role in assisting users in discovering relevant information from a digital library.
Purpose: This study aims to provide a bibliometric analysis of research publications on recommender systems for digital libraries.
Method: The research method involved the quantitative bibliometric approach to analyse the research publications from the Web of Science and Scopus databases using Biblioshiny. The dataset retrieved comprises 374 documents published between the period of 2004-2023.
Findings: The study's findings highlight that the number of publications was notably high in 2009 and 2018 within the analyzed period. The most significant contributors are Porcel C, Herrera-Viedma E, and Beel J. There is a considerable international collaboration between the countries- China, USA, and Germany.
Conclusions: This study indicates an emerging interest in recommender systems for digital libraries with the continuous evolution of new recommendation models. There is a huge potential for research considering the availability of multimodal data, the continuous evolution of new technologies such as deep learning, and opportunities for the development of an architecture for integrating different digital libraries.
Downloads
Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2021). Effects of personalized and aggregate top-N recommendation lists on user preference ratings. ACM Transactions on Information Systems (TOIS), 39(2), 1-38.
Ajij, M., Roy, D. S., & Pratihar, S. (2023). Automated generation of text handles from scanned images of scholarly articles for indexing in digital archive. Multimedia Tools and Applications, 82(15), 22373-22404.
Akbar, M., Shaffer, C. A., Fan, W., & Fox, E. A. (2014, September). Recommendation based on Deduced Social Networks in an educational digital library. In IEEE/ACM Joint Conference on Digital Libraries (pp. 29-38). IEEE.
Almaghrabi, M., & Chetty, G. (2020, December). Deep Machine Learning Digital library recommendation system based on Metadata for Arabic and English Languages. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE
Al-Qallaf, C. L., & Ridha, A. (2019). A comprehensive analysis of academic library websites: design, navigation, content, services, and web 2.0 tools. International Information & Library Review, 51(2), 93-106.
Amolochitis, E. (2018). Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers.
Anas, A., & Salim, T. A. (2023). Digital curation at the university: Systematic literature review analysis with a bibliometric approach through the scopus database for the period 2012-2022. Record and Library Journal, 9(2), 347-358
Awal, G. K., & Bharadwaj, K. K. (2014). Team formation in social networks based on collective intelligence–an evolutionary approach. Applied intelligence, 41, 627-648.
Awal, G. K., & Bharadwaj, K. K. (2019). Leveraging collective intelligence for behavioral prediction in signed social networks through evolutionary approach. Information Systems Frontiers, 21, 417-439.
Beierle, F., Aizawa, A., & Beel, J. (2017). Exploring choice overload in related-article recommendations in digital libraries. arXiv preprint arXiv:1704.00393.
Burke, R., O'Mahony, M. P., & Hurley, N. J. (2015). Robust collaborative recommendation. Recommender systems handbook, 961-995.
Chu, Z., Hao, H., Ouyang, X., Wang, S., Wang, Y., Shen, Y., ... & Li, S. (2023). Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837.
Dias, L. L., Barrére, E., & de Souza, J. F. (2021). The impact of semantic annotation techniques on content-based video lecture recommendation. Journal of Information Science, 47(6), 740-752.
Fu, Q., Fu, J., & Wang, D. (2022, January). Deep Learning and Data Mining for Book Recommendation: Retrospect and Expectation. In 2022 14th International Conference on Computer Research and Development (ICCRD) (pp. 60-64). IEEE.
Ghosal, T., Chakraborty, A., Sonam, R., Ekbal, A., Saha, S., & Bhattacharyya, P. (2019, June). Incorporating full text and bibliographic features to improve scholarly journal recommendation. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) (pp. 374-375). IEEE.
Kato, A., Kisangiri, M., & Kaijage, S. (2021). A review development of digital library resources at university level. Education Research International, 2021, 1-13.
Liu, M. (2022). Personalized Recommendation System Design for Library Resources through Deep Belief Networks. Mobile Information Systems, 2022.
Liu, Y. (2021, September). Construction of personalized recommendation system of university library based on SOM neural network. In 2021 4th International Conference on Information Systems and Computer Aided Education (pp. 2199-2203).
Magara, M. B., Ojo, S., & Zuva, T. (2017, December). Toward altmetric-driven research-paper recommender system framework. In 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 63-68). IEEE.
Mo, D., Chen, X. G., Duan, S., Wang, L. D., Wu, Q., Zhang, M., & Xie, L. (2019, August). Personalized resource recommendation based on collaborative filtering algorithm. In Journal of Physics: Conference Series (Vol. 1302, No. 2, p. 022025). IOP Publishing.
Muntiah, A., & Dewi, A. O. P. (2023). Research Dissemination of Indonesian Institute of Sciences (LIPI) 2017-2020: A Bibliometric Profile. Record and Library Journal, 9(2), 334-346
Nugraha, E., Ardiansyah, T., Junaeti, E., & Riza, L. S. (2020). Enhanced Digital Library with Book Recommendations Based on Collaborative Filtering. Journal of Engineering Education Transformations, 34(Special Issue).
Omisore, M. O., & Samuel, O. W. (2014). Personalized recommender system for digital libraries. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 9(1), 18-32.
Otegi, A., Agirre, E., & Clough, P. (2014, September). Personalised PageRank for making recommendations in digital cultural heritage collections. In IEEE/ACM Joint Conference on Digital Libraries (pp. 49-52). IEEE.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International journal of surgery, 88, 105906.
Porcel, C., Ching-Lopez, A., Bernabe-Moreno, J., Tejeda-Lorente, A., & Herrera-Viedma, E. (2017). Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries. Journal of Information Processing Systems, 13(4).
Porcel, C., Ching-López, A., Tejeda-Lorente, A., Bernabé-Moreno, J., & Herrera-Viedma, E. (2018). Analysis of different proposals to improve the dissemination of information in university digital libraries. In Advances in Fuzzy Logic and Technology 2017: Proceedings of: EUSFLAT-2017–The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN'2017–The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 3 10 (pp. 195-206). Springer International Publishing.
Rakhmatullaev, M., Normatov, S., & Bekkamov, F. (2023, June). Fuzzy Relations Based Intelligent Information Retrieval for Digital Library Users. In ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference (Vol. 2, pp. 80-83).
Rhanoui, M., Mikram, M., Yousfi, S., Kasmi, A., & Zoubeidi, N. (2022). A hybrid recommender system for patron driven library acquisition and weeding. Journal of King Saud University-Computer and Information Sciences, 34(6), 2809-2819.
Roy, S., Biswas, M., & De, D. (2020). iMusic: a session-sensitive clustered classical music recommender system using contextual representation learning. Multimedia Tools and Applications, 79, 24119-24155.
Schubotz, M., Greiner-Petter, A., Scharpf, P., Meuschke, N., Cohl, H. S., & Gipp, B. (2018, May). Improving the representation and conversion of mathematical formulae by considering their textual context. In Proceedings of the 18th ACM/IEEE on joint conference on digital libraries (pp. 233-242).
Senthil Kumaran, V., & Latha, R. (2023). Towards personal learning environment by enhancing adaptive access to digital library using ontology-supported collaborative filtering. Library Hi Tech, 41(6), 1658-1675.
Shen, Y. H. (2018). Design of Digital Network Shared Learning Platform based on SCORM Standard. International Journal of Emerging Technologies in Learning, 13(7).
Shi, Y., & Zhu, Y. (2020). Research on aided reading system of digital library based on text image features and edge computing. IEEE Access, 8, 205980-205988.
Smith, J., Weeks, D., Jacob, M., Freeman, J., & Magerko, B. (2019, March). Towards a Hybrid Recommendation System for a Sound Library. In IUI workshops (Vol. 19).
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of business research, 104, 333-339.
Sonboli, N., Burke, R., Liu, Z., & Mansoury, M. (2020, September). Fairness-aware Recommendation with librecauto. In Proceedings of the 14th ACM Conference on Recommender Systems (pp. 594-596).
Stiller, J., Petras, V., Gäde, M., & Isaac, A. (2014). Automatic enrichments with controlled vocabularies in Europeana: Challenges and consequences. In Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection: 5th International Conference, EuroMed 2014, Limassol, Cyprus, November 3-8, 2014. Proceedings 5 (pp. 238-247). Springer International Publishing.
Tejeda-Lorente, A., Bernabé-Moreno, J., Porcel, C., & Herrera-Viedma, E. (2014a). Integrating quality criteria in a fuzzy linguistic recommender system for digital libraries. Procedia Computer Science, 31, 1036-1043.
Tejeda-Lorente, A., Bernabé-Moreno, J., Porcel, C., & Herrera-Viedma, E. (2018). Using bibliometrics and fuzzy linguistic modeling to deal with cold start in recommender systems for digital libraries. In Advances in Fuzzy Logic and Technology 2017: Proceedings of: EUSFLAT-2017–The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN'2017–The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 3 10 (pp. 393-404). Springer International Publishing.
Tejeda-Lorente, Á., Porcel, C., Peis, E., Sanz, R., & Herrera-Viedma, E. (2014b). A quality based recommender system to disseminate information in a university digital library. Information Sciences, 261, 52-69.
Troussas, C., Krouska, A., Koliarakis, A., & Sgouropoulou, C. (2023). Harnessing the power of user-centric artificial intelligence: Customized recommendations and personalization in hybrid recommender systems. Computers, 12(5), 109.
Zhao, L. (2021). Personalized recommendation by using fused user preference to construct smart library. Internet Technology Letters, 4(3), e273.
Zhao, Z., Fan, W., Li, J., Liu, Y., Mei, X., Wang, Y., ... & Li, Q. (2024). Recommender systems in the era of large language models (llms). IEEE Transactions on Knowledge and Data Engineering.
Copyright (c) 2024 Gaganmeet Kaur Awal, Ujjwal Tehlan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Record and Library Journal by Unair is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
1. The journal allows the author to hold the copyright of the article without restrictions.
2. The journal allows the author(s) to retain publishing rights without restrictions
3. The legal formal aspect of journal publication accessibility refers to Creative Commons Attribution Share-Alike (CC BY-SA).
4. The Creative Commons Attribution Share-Alike (CC BY-SA) license allows re-distribution and re-use of a licensed work on the conditions that the creator is appropriately credited and that any derivative work is made available under "the same, similar or a compatible license”. Other than the conditions mentioned above, the editorial board is not responsible for copyright violation.
