Recommender Systems in Libraries: A Systematic Review of Service Quality and Engagement

Ludia Rosema Dewi, Rajesri Govindaraju, Destina Ratna Asih Khodijah Kadarsah

Abstract


This study presents a systematic literature review (SLR) on recommender systems (RS) in library contexts, covering both service-provider perspectives and user engagement. Using the PRISMA framework, 14 studies from ScienceDirect and IEEE Xplore were synthesized to map dominant use cases, data sources, and algorithmic strategies. Hybrid approaches combining collaborative filtering and content-based filtering are most frequently reported, supporting libraries in mitigating cold-start and sparsity in circulation data. Fuzzy linguistic modeling and association rule mining are also used to handle imprecise preferences and identify co-borrowing patterns. Emerging work highlights knowledge graphs and graph neural networks for capturing semantic relationships between readers and collection attributes. Evaluation commonly relies on offline metrics (e.g., precision, recall, NDCG) and is complemented by user studies in fewer cases. The review provides practical recommendations for phased, privacy-aware RS deployment in regional and public library services. Key implementation enablers and barriers include metadata quality, integration with OPAC/digital portals, and privacy-ethics governance for using user interaction and borrowing logs.

Keywords


Engagement; libraries; PRISMA; recommender systems; systematic review.

Full Text:

PDF

References


Al-Ghuribi, S. M., Noah, S. A., & Mohammed, M. A. (2023). An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets. PeerJ Computer Science, 9, e1525. https://doi.org/10.7717/peerj-cs.1525.

Ansari, Nasim, Hossein Vakilimofrad, Muharram Mansoorizadeh, and Mohamad Reza Amiri. 2020. “Using Data Mining Techniques to Predict User’s Behavior and Create Recommender Systems in the Libraries and Information Centers.” Global Knowledge, Memory and Communication 70(6–7):538–57. doi:10.1108/GKMC-04-2020-0058.

Anwar, Khalid, Jamshed Siddiqui, and Shahab Saquib Sohail. 2020. Machine Learning-Based Book Recommender System: A Survey and New Perspectives. Vol. 13.

Awal, G. K., & Tehlan, U. (2024). Mapping the research landscape of recommender systems for digital libraries: A bibliometric analysis of two decades (2004–2023). Record and Library Journal, 10(1), 180–194. https://doi.org/10.20473/rlj.V10-I1.2024.180-194.

Bhoi, N., & Choudhury, B. B. (2021). A survey on applications of recommender systems in higher education. Education and Information Technologies, 26(4), 4103–4131. https://doi.org/10.1007/s10639-021-10520-x.

Chen, L., Wu, X., & He, L. (2015). Developing a book recommender system for an academic library using circulation data and content analysis. Library Hi Tech, 33(2), 263–276. https://doi.org/10.1108/LHT-09-2014-0089.

Doğan, O., Yalcin, E., & Hızıroğlu, O. A. (2024). Digitalization for enhancing reading habits: The improved hybrid book recommendation system with genre-oriented profiles. Library Management, 45(8/9), 489–505. https://doi.org/10.1108/LM-03-2024-0030.

Gao, W. (2025). Research on optimization of library book recommendation system based on the collaborative fusion of Transformer architecture and adaptive extreme learning machine. Systems and Soft Computing, 7, 200287. https://doi.org/10.1016/j.sasc.2025.200287.

Gupta, V., & Pandey, S. R. (2019). Recommender systems for digital libraries: A review of concepts and concerns. Library Philosophy and Practice, Article 2417. (Available in the University of Nebraska–Lincoln digital repository).

Harisanty, D., Anna, N. E. V., Putri, T. E., Firdaus, A. A., & Noor Azizi, N. A. (2023). Is adopting artificial intelligence in libraries urgency or a buzzword? A systematic literature review. Journal of Information Science, 49(1), 23–35. https://doi.org/10.1177/01655515221085860.

Hassan, M. U., Shuib, L., & Ismail, M. A. (2018). A literature survey on recommender systems, techniques and application fields. Journal of Information Science, 44(6), 733–758. https://doi.org/10.1177/0165551517722129.

Hikmatyar, Missi and Ruuhwan. 2020. “Book Recommendation System Development Using User-Based Collaborative Filtering.” in Journal of Physics: Conference Series. Vol. 1477. Institute of Physics Publishing.

Ifada, N., Susanto, L. R., Saputro, P. A., Nugraha, A. P., Muflikhah, L., Fauzi, M. A., & Adinugroho, S. (2019). Enhancing the performance of library book recommendation system by employing the probabilistic-keyword model on a collaborative filtering approach. Procedia Computer Science, 157, 345–352. https://doi.org/10.1016/j.procs.2019.08.176.

Jomsri, P., Prangchumpol, D., Poonsilp, K., & Panityakul, T. (2023). Hybrid recommender system model for digital library from multiple online publishers. F1000Research, 12, 1140. https://doi.org/10.12688/f1000research.133013.3.

Jomsri, Pijitra. 2018. “FUCL Mining Technique for Book Recommender System in Library Service.” Pp. 550–57 in Procedia Manufacturing. Vol. 22. Elsevier B.V.

Kassenkhan, A. (2026). A KNN-based bilingual book recommendation system with gamification and learning analytics. Information, 17(2), 120. https://doi.org/10.3390/info17020120.

Kreutz, C. K., & Schenkel, R. (2022). Scientific paper recommendation systems: A literature review of recent publications. International Journal on Digital Libraries, 23(3), 335–369. https://doi.org/10.1007/s00799-022-00339-w.

Li, Z., Xiao, Q., Liu, Y., Lin, Y., & Luo, F. (2023). BookGPT: A general framework for book recommendation empowered by large language model. Electronics, 12(22), 4654. https://doi.org/10.3390/electronics12224654

Lu, J., Wu, D., Mao, M., Wang, W., & 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.

Morawski, J., Stepan, T., Dick, S., & Miller, J. (2017). A fuzzy recommender system for public library catalogs. International Journal of Intelligent Systems, 32(10), 1062–1084. https://doi.org/10.1002/int.21884.

Nart, D. De, and C. Tasso. 2014. “A Personalized Concept-Driven Recommender System for Scientific Libraries.” Pp. 84–91 in Procedia Computer Science. Vol. 38. Elsevier B.V.

Neamatollahi, Z., & Danesh, F. (2026). AI-driven personalization in library and information services: A systematic review of techniques, user outcomes, and ethical considerations. The Journal of Academic Librarianship, 52(1), Article 103195. https://doi.org/10.1016/j.acalib.2025.103195.

Porcel, C., and E. Herrera-Viedma. 2010. “Dealing with Incomplete Information in a Fuzzy Linguistic Recommender System to Disseminate Information in University Digital Libraries.” Knowledge-Based Systems 23(1):32–39. doi:10.1016/j.knosys.2009.07.007.

Porcel, C., J. M. Moreno, and E. Herrera-Viedma. 2009. “A Multi-Disciplinar Recommender System to Advice Research Resources in University Digital Libraries.” Expert Systems with Applications 36(10):12520–28. doi:10.1016/j.eswa.2009.04.038.

Porter, M. A., & Davis, C. A. (2018). Account-based recommenders in open discovery environments: A machine learning approach. Code4Lib Journal, (42). (Online journal article, no pagination).

Raghavendra, C. K., & Srikantaiah, K. C. (2022). Switching hybrid model for personalized recommendations by combining users’ demographic information. Journal of Theoretical and Applied Information Technology, 100(3), 825–835. (ISSN 1992-8645).

Rana, A., & Deeba, K. (2019). Online book recommendation system using collaborative filtering (with Jaccard similarity). Journal of Physics: Conference Series, 1362(1), 012130. https://doi.org/10.1088/1742-6596/1362/1/012130.

Rhanoui, Maryem, Mounia Mikram, Siham Yousfi, Ayoub Kasmi, and Naoufel Zoubeidi. 2022. “A Hybrid Recommender System for Patron Driven Library Acquisition and Weeding.” Journal of King Saud University - Computer and Information Sciences 34(6):2809–19. doi:10.1016/j.jksuci.2020.10.017.

Ricci, Francesco, and Rokach Lior. 2015. Recommender Systems Handbook Second Edition. Second. edited by S. Bracha. Springer.

Roy, Deepjyoti, and Mala Dutta. 2022. “A Systematic Review and Research Perspective on Recommender Systems.” Journal of Big Data 9(1). doi:10.1186/s40537-022-00592-5.

Roy, Falguni, Na Zhao, and Xiaofeng Ding. 2025. “SBlur: An Obfuscation Approach for Preserving Sensitive Attributes in Recommender System.” Information Processing and Management 62(6). doi:10.1016/j.ipm.2025.104282.

Saadi, Yakoub, and Aymen Chraf. 2023. The Design of a Book Recommender System. Mohamed El Bachir El Ibrahimi University of Borj Bou Arréridj.

Saifudin, Ilham, and Triyanna Widiyaningtyas. 2024. “Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets.” IEEE Access 12:19827–47. doi:10.1109/ACCESS.2024.3359274.

Saini, Kapil, and Ajmer Singh. 2024. “A Content-Based Recommender System Using Stacked LSTM and an Attention-Based Autoencoder.” Measurement: Sensors 31. doi:10.1016/j.measen.2023.100975.

Sejwal, Vineet K., and Muhammad Abulaish. 2022. “A Hybrid Recommendation Technique Using Topic Embedding for Rating Prediction and to Handle Cold-Start Problem.” Expert Systems with Applications 209. doi:10.1016/j.eswa.2022.118307.

Serrano-Guerrero, Jesus, Enrique Herrera-Viedma, Jose A. Olivas, Andres Cerezo, and Francisco P. Romero. 2011. “A Google Wave-Based Fuzzy Recommender System to Disseminate Information in University Digital Libraries 2.0.” Information Sciences 181(9):1503–16. doi:10.1016/j.ins.2011.01.012.

Shi, X., Hao, C., Yue, D., & Lu, H. (2024). Library book recommendation with CNN-FM deep learning approach. Library Hi Tech, 42(5), 1559–1578. https://doi.org/10.1108/LHT-08-2022-0400.

Simović, Aleksandar. 2018. “A Big Data Smart Library Recommender System for an Educational Institution.” Library Hi Tech 36(3):498–523. doi:10.1108/LHT-06-2017-0131.

Sugiyama, K., & Kan, M.-Y. (2015). Scholarly paper recommendation — A survey and new perspectives. International Journal on Digital Libraries, 16(3–4), 175–199. https://doi.org/10.1007/s00799-015-0156-0.

Tarus, J. K., Niu, Z., & Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72, 37–48. https://doi.org/10.1016/j.future.2017.02.049.

Tejeda-Lorente, A., J. Bernabé-Moreno, C. Porcel, and E. Herrera-Viedma. 2014. “Integrating Quality Criteria in a Fuzzy Linguistic Recommender System for Digital Libraries.” Pp. 1036–43 in Procedia Computer Science. Vol. 31. Elsevier B.V.

Tejeda-Lorente, Álvaro, Carlos Porcel, Eduardo Peis, Rosa Sanz, and Enrique Herrera-Viedma. 2014. “A Quality Based Recommender System to Disseminate Information in a University Digital Library.” Information Sciences 261:52–69. doi:10.1016/j.ins.2013.10.036.

Tian, Y., Zheng, B., Wang, Y., Zhang, Y., & Wu, Q. (2019). College library personalized recommendation system based on hybrid recommendation algorithm. Procedia CIRP, 83, 490–494. https://doi.org/10.1016/j.procir.2019.04.126.

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.

Wang, Z., & Wang, Y. (2024). Digital library book recommendation system based on tag mining. Journal of Artificial Intelligence Research, 1(1), 19–32. https://doi.org/10.70891/JAIR.2024.100022.

Xiao, J., & Gao, W. (2020). Connecting the dots: Reader ratings, bibliographic data, and machine-learning algorithms for monograph selection. The Serials Librarian, 78(1–4), 1–6. https://doi.org/10.1080/0361526X.2020.1707599.

Xiao, Junchao, Linhui Wu, Fuli Zhong, and Jinling Zhang. 2026. “Knowledge-Aware Neighbor Collaborative Multi-Relationship Multi-Interest Comparative Recommender System for Diversified Book Recommendations.” Expert Systems with Applications 297. doi:10.1016/j.eswa.2025.129235.

Ye, J. (2023). A group recommender system for books based on fine-grained classification of comments. The Electronic Library, 41(2/3), 326–346. https://doi.org/10.1108/EL-07-2022-0153.

Zamzami, L., Prastowo, A. A., Rulinawaty, & Rahim, R. (2021). Analysis of library book borrower patterns using Apriori association data mining techniques. Library Philosophy and Practice, Article 6632. (Available online at University of Nebraska–Lincoln Digital Commons).

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), Article 5. https://doi.org/10.1145/3285029.




DOI: https://doi.org/10.17509/edulib.v16i1.99078

DOI (PDF): https://doi.org/10.17509/edulib.v16i1.99078.g36413

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Edulib

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

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

merahtoto

 

merahtoto

 

merahtoto