{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:07:21Z","timestamp":1769576841857,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSSF","award":["22BTQ033"],"award-info":[{"award-number":["22BTQ033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Traditional book recommendation methods predominantly rely on collaborative filtering and context-based approaches. However, existing methods fail to account for the order of users\u2019 book borrowings and the duration they hold them, both of which are crucial indicators reflecting users\u2019 book preferences. To address this challenge, we propose a book recommendation framework called DPBD, which disentangles preferences based on borrowing duration, thereby explicitly modeling temporal patterns in library borrowing behaviors. The DPBD model adopts a dual-path neural architecture comprising the following: (1) The item-level path utilizes self-attention networks to encode historical borrowing sequences while incorporating borrowing duration as an adaptive weighting mechanism for attention score refinement. (2) The feature-level path employs gated fusion modules to effectively aggregate multi-source item attributes (e.g., category and title), followed by self-attention networks to model feature transition patterns. The framework subsequently combines both path representations through fully connected layers to generate user preference embeddings for next-book recommendation. Extensive experiments conducted on two real-world university library datasets demonstrate the superior performance of the proposed DPBD model compared with baseline methods. Specifically, the model achieved 13.67% and 15.75% on HR@1 and 15.75% and 12.90% on NDCG@1 across the two datasets.<\/jats:p>","DOI":"10.3390\/bdcc9090222","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T09:31:12Z","timestamp":1756373472000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DPBD: Disentangling Preferences via Borrowing Duration for Book Recommendation"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5525-904X","authenticated-orcid":false,"given":"Zhifang","family":"Liao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}]},{"given":"Liping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}]},{"given":"Yuelan","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"Communist Youth League Committee, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/978-981-15-0184-5_29","article-title":"Collaborative filtering for book recommendation system","volume":"Volume 2","author":"Ramakrishnan","year":"2020","journal-title":"Soft Computing for Problem Solving: SocProS 2018"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mathew, P., Kuriakose, B., and Hegde, V. (2016, January 16\u201318). Book Recommendation System through content based and collaborative filtering method. Proceedings of the 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, India.","DOI":"10.1109\/SAPIENCE.2016.7684166"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mooney, R.J., and Roy, L. (2000, January 2\u20137). Content-based book recommending using learning for text categorization. Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, USA.","DOI":"10.1145\/336597.336662"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kang, W.C., and McAuley, J. (2018, January 17\u201320). Self-attentive sequential recommendation. Proceedings of the 2018 IEEE International Conference on Data Mining, Singapore.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Y., and McAuley, J. (2020, January 3\u20137). Time interval aware self-attention for sequential recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, TX, USA.","DOI":"10.1145\/3336191.3371786"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P. (2019, January 3\u20137). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3357895"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vaz, P.C., Ribeiro, R., and de Matos, D.M. (2013, January 11\u201312). Understanding temporal dynamics of ratings in the book recommendation scenario. Proceedings of the 2013 International Conference on Information Systems and Design of Communication, Lisboa, Portugal.","DOI":"10.1145\/2503859.2503862"},{"key":"ref_8","first-page":"117","article-title":"CF Recommending Model Based on Borrowing-time Scores and Its Application","volume":"56","author":"Jing","year":"2012","journal-title":"Libr. Inf. Serv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jomsri, P. (2014, January 13\u201315). Book recommendation system for digital library based on user profiles by using association rule. Proceedings of the Fourth edition of the International Conference on the Innovative Computing Technology (INTECH 2014), Luton, UK.","DOI":"10.1109\/INTECH.2014.6927766"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/138859.138867","article-title":"Using collaborative filtering to weave an information tapestry","volume":"35","author":"Goldberg","year":"1992","journal-title":"Commun. ACM"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001, January 1\u20135). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China.","DOI":"10.1145\/371920.372071"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tewari, A.S., Kumar, A., and Barman, A.G. (2014, January 21\u201322). Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. Proceedings of the 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India.","DOI":"10.1109\/IAdCC.2014.6779375"},{"key":"ref_13","first-page":"133","article-title":"Precise Book Recommendation Based on SOM Neural Network and Ranking Factorization Machine","volume":"42","author":"Ding","year":"2019","journal-title":"Inf. Stud. Theory Appl."},{"key":"ref_14","first-page":"201","article-title":"Deep Learning Recommendation Algorithm Based on Reader Preference Analysis","volume":"45","author":"LIU","year":"2023","journal-title":"J. Southwest Univ. Nat. Sci. Ed."},{"key":"ref_15","first-page":"76","article-title":"Personalized Recommendation of Online Book Resources Based on Deep Distance Decomposition","volume":"39","author":"Huang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1109\/ACCESS.2016.2564997","article-title":"A personalized time-sequence-based book recommendation algorithm for digital libraries","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3426723","article-title":"Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations","volume":"39","author":"Fang","year":"2020","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., and Orgun, M. (2019). Sequential recommender systems: Challenges, progress and prospects. arXiv.","DOI":"10.24963\/ijcai.2019\/883"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2010, January 26\u201330). Factorizing personalized markov chains for next-basket recommendation. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772773"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, R., and McAuley, J. (2016, January 12\u201315). Fusing similarity models with markov chains for sparse sequential recommendation. Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0030"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., and Jing, H. (2017, January 6\u201310). Recurrent recommender networks. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, UK.","DOI":"10.1145\/3018661.3018689"},{"key":"ref_22","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tang, J., and Wang, K. (2018, January 5\u20139). Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Los Angeles, CA, USA.","DOI":"10.1145\/3159652.3159656"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., and He, X. (2019, January 11\u201315). A simple convolutional generative network for next item recommendation. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, Australia.","DOI":"10.1145\/3289600.3290975"},{"key":"ref_25","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., and Tan, T. (February, January 27). Session-based recommendation with graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_26","first-page":"4741","article-title":"Dynamic graph neural networks for sequential recommendation","volume":"35","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, M., Ma, W., Liu, Y., and Ma, S. (2020, January 25\u201330). Make it a chorus: Knowledge-and time-aware item modeling for sequential recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.","DOI":"10.1145\/3397271.3401131"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, Z., Liu, J., Luo, X., Huang, Z., Zhu, Q., and Che, W. (2025). Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning. arXiv.","DOI":"10.1145\/3711896.3736835"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"123876","DOI":"10.1016\/j.eswa.2024.123876","article-title":"A review of recommender systems based on knowledge graph embedding","volume":"250","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_30","first-page":"26701","article-title":"Llm-esr: Large language models enhancement for long-tailed sequential recommendation","volume":"37","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","unstructured":"Zhai, J., Zheng, X., Wang, C.D., Li, H., and Tian, Y. (November, January 29). Knowledge prompt-tuning for sequential recommendation. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, NIPS Foundation."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, C., Li, X., Cai, G., Dong, Z., Zhu, H., and Shang, L. (2021, January 2\u20139). Noninvasive self-attention for side information fusion in sequential recommendation. Proceedings of the the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i5.16549"},{"key":"ref_34","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_35","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","unstructured":"Pazzani, M.J., and Billsus, D. (2007). Content-based recommendation systems. The Adaptive Web: Methods and Strategies of Web Personalization, Springer."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","article-title":"Amazon. com recommendations: Item-to-item collaborative filtering","volume":"7","author":"Linden","year":"2003","journal-title":"IEEE Internet Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, J., De Vries, A.P., and Reinders, M.J. (2006, January 6\u201311). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, USA.","DOI":"10.1145\/1148170.1148257"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/9\/222\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:34:18Z","timestamp":1760034858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/9\/222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["bdcc9090222"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9090222","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,28]]}}}