Analysis Of A Textbook Recommendation System Based On Semantic Search And Full-Text Retrieval Based On Students' Learning Materials At The Labuang State Vocational High School Library
Abstract
School libraries play a crucial role in providing relevant learning resources for students. However, book search systems based on titles or simple keywords often fail to understand learning needs contextually. This study aims to develop a textbook recommendation system based on semantic search and full-text retrieval based on student learning materials at the Labuang State Vocational High School Library.
This study utilizes Natural Language Processing (NLP) techniques in the text pre-processing stage and uses the Term Frequency–Inverse Document Frequency (TF-IDF) method and sentence embedding to represent text. The level of similarity between teaching materials and book content is calculated using the cosine similarity method, then combined through a Hybrid Retrieval approach to produce a book recommendation relevance score. The research dataset consists of 80 textbooks that are processed into 21,556 text paragrap hs as the basis for the system's search.
System performance evaluation was conducted using Precision, Recall, F1-Score, and Mean Average Precision (MAP) metrics to measure the relevance and quality of document rankings generated by the system. The results showed that the Hybrid Retrieval approach was capable of generating relevant book recommendations based on student learning materials. The developed system is expected to assist students in finding books that meet their learning needs and improve the effectiveness of information searches in school libraries.
References
R. Ardiansyah, M. A. Bianto, and B. D. Saputra, “Sistem Rekomendasi Buku Perpustakaan Sekolah Menggunakan Metode Content-Based Filtering,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 4, no. 2, pp. 510–518, Oct. 2023, doi: 10.37859/coscitech.v4i2.5131.
L. Rosidah and P. Dellia, “Library Book Recommendation System Using Content-Based Filtering,” Internet of Things and Artificial Intelligence Journal, vol. 4, no. 1, pp. 42–65, Feb. 2024, doi: 10.31763/iota.v4i1.693.
A. Askari, A. Abolghasemi, G. Pasi, W. Kraaij, and S. Verberne, “Injecting the BM25 Score as Text Improves BERT-Based Re-rankers,” in Advances in Information Retrieval, 45th European Conference on Information Retrieval (ECIR 2023), Dublin, Ireland, Apr. 2023, vol. 13980, pp. 66–83, doi: 10.1007/978-3-031-28244-7_5.
C. G. Reswara, J. Nicolas, I. M. D. Widyatama, D. David, and P. Arisaputra, “Book Recommendation System Using TF-IDF and Cosine Similarity,” AIP Conference Proceedings, vol. 3135, no. 1, p. 020003, May 2024, doi: 10.1063/5.0212477.
H. S. Walsh and S. R. Andrade, “Semantic Search With Sentence-BERT for Design Information Retrieval,” in Volume 2: 42nd Computers and Information in Engineering Conference (CIE), St. Louis, Missouri, USA, Aug. 2022, p. V002T02A066, doi: 10.1115/DETC2022-89557.
G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave, “Unsupervised Dense Information Retrieval With Contrastive Learning,” Transactions on Machine Learning Research, 2022.
D. Lee, S.-W. Hwang, K. Lee, S. Choi, and S. Park, “On Complementarity Objectives for Hybrid Retrieval,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada, Jul. 2023, pp. 13357–13368, doi: 10.18653/v1/2023.acl-long.746.
J. Lu, K. Hall, J. Ma, and J. Ni, “HYRR: Hybrid Infused Reranking for Passage Retrieval,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy, May 2024, pp. 8528–8534.
P. Ouanlamai, T. Panyatip, P. Phothinam, A. Sumungkaset, N. Masusai, S. Phimphisan, and P. Thipsanthia, “Book Recommendation System Using Content-Based Filtering Technique,” Journal of Engineering and Industrial Technology, Kalasin University, vol. 3, no. 3, pp. 36–53, Jun. 2025, doi: 10.14456/jeit.2025.13.
A. Bhajantri, N. K. Goudar, D. G. M., R. B. Kaliwal, V. Rathod, A. Kulkarni, and G. K., “Personalized Book Recommendations: A Hybrid Approach Leveraging Collaborative Filtering, Association Rule Mining, and Content-Based Filtering,” EAI Endorsed Transactions on Internet of Things, vol. 10, no. 1, 2024, doi: 10.4108/eetiot.6996.
N. N. Doan, A. Härmä, R. Celebi, and V. Gottardo, “A Hybrid Retrieval Approach for Advancing Retrieval-Augmented Generation Systems,” in Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024), Trento, Italy, Oct. 2024, pp. 397–409.
D. Ridhwanullah, Y. K. Kumarahadi, and B. D. Raharja, “Content-Based Filtering pada Sistem Rekomendasi Buku Informatika,” Jurnal Ilmiah SINUS, vol. 22, no. 2, pp. 57–66, Jul. 2024, doi: 10.30646/sinus.v22i2.840.
E. Nikolaev, I. Bondarenko, I. Aushev, V. Krikunov, A. Glinskii, V. Konovalov, and J. Belikova, “FactDebug at SemEval-2025 Task 7: Hybrid Retrieval Pipeline for Identifying Previously Fact-Checked Claims Across Multiple Languages,” in Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), 2025, pp. 2190–2196.
M.-C. Lee, Q. Zhu, C. Mavromatis, Z. Han, S. Adeshina, V. N. Ioannidis, H. Rangwala, and C. Faloutsos, “HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases,” in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025, pp. 879–893, doi: 10.18653/v1/2025.acl-long.43.
N. Saputri and P. Suryati, “Machine Learning-Based Book and Library Recommendation Application Using Content-Based Filtering Method,” Jurnal Teknologi Informasi dan Terapan, vol. 12, no. 1, pp. 25–33, Jun. 2025, doi: 10.25047/jtit.v12i1.445.
M. F. Roziq, R. N. Sarbini, H. Mukminna, and M. S. Anam, “Developing a Content-Based Book Recommendation System for School E-Libraries Using TF-IDF and Cosine Similarity,” Jurnal Ilmiah Informatika dan Komputer, vol. 3, no. 1, pp. 49–57, Jun. 2026, doi: 10.69533/informatech.volume3number1.543.
