{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:45:47Z","timestamp":1760060747913,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"],"award-info":[{"award-number":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Education Commission Research Plan","award":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"],"award-info":[{"award-number":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"]}]},{"name":"Hebei Technology Innovation Center of Cultural Tourism Big Data","award":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"],"award-info":[{"award-number":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"]}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Science and Technology Project","doi-asserted-by":"publisher","award":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"],"award-info":[{"award-number":["62172352","2023KJ203","SG2019036-zd202206","23YDTPJC00320"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In recent years, the remarkable versatility of large language models (LLMs) has spurred considerable interest in leveraging their capabilities for recommendation systems. Critically, we argue that the intrinsic aptitude of LLMs for modeling sequential patterns and temporal dynamics renders them uniquely suited for sequential recommendation tasks\u2014a foundational premise explored in depth later in this work. This potential, however, is tempered by significant hurdles: a discernible gap exists between the general competencies of conventional LLMs and the specialized needs of recommendation tasks, and their capacity to uncover complex, latent data interrelationships often proves inadequate, potentially undermining recommendation efficacy. To bridge this gap, our approach centers on adapting LLMs through fine-tuning on dedicated recommendation datasets, enhancing task-specific alignment. Further, we present the temporal Integration Enhanced Fine-Tuning of Large Language Models for Sequential Recommendation (TisLLM) framework. TisLLM specifically targets the deeper excavation of implicit associations within recommendation data streams. Its core mechanism involves partitioning sequential user interaction data using temporally defined sliding windows. These chronologically segmented slices are then aggregated to form enriched contextual representations, which subsequently drive the LLM fine-tuning process. This methodology explicitly strengthens the model\u2019s compatibility with the inherently sequential nature of recommendation scenarios. Rigorous evaluation on benchmark datasets provides robust empirical validation, confirming the effectiveness of the TisLLM framework.<\/jats:p>","DOI":"10.3390\/info16090818","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T12:08:03Z","timestamp":1758542883000},"page":"818","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TisLLM: Temporal Integration-Enhanced Fine-Tuning of Large Language Models for Sequential Recommendation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2790-3210","authenticated-orcid":false,"given":"Xiaosong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"},{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin 300222, China"},{"name":"The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province, Langfang 065399, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"},{"name":"Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin 300222, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Technology Center, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liqing","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"ref_1","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zheng, B., Hou, Y., Lu, H., Chen, Y., Zhao, W.X., Chen, M., and Wen, J.R. (2024, January 13\u201316). Adapting large language models by integrating collaborative semantics for recommendation. Proceedings of the 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, The Netherlands.","DOI":"10.1109\/ICDE60146.2024.00118"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, J., Shan, R., Zhu, C., Du, K., Chen, B., Quan, S., Tang, R., Yu, Y., and Zhang, W. (2024, January 13\u201317). Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation. Proceedings of the ACM on Web Conference 2024, Singapore.","DOI":"10.1145\/3589334.3645467"},{"key":"ref_4","first-page":"1","article-title":"Recommendation as instruction following: A large language model empowered recommendation approach","volume":"43","author":"Zhang","year":"2023","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s11280-024-01291-2","article-title":"A survey on large language models for recommendation","volume":"27","author":"Wu","year":"2024","journal-title":"World Wide Web"},{"key":"ref_6","unstructured":"Wang, L., and Lim, E.P. (2023). Zero-shot next-item recommendation using large pretrained language models. arXiv."},{"key":"ref_7","unstructured":"Zhang, Y., Feng, F., Zhang, J., Bao, K., Wang, Q., and He, X. (2023). Collm: Integrating collaborative embeddings into large language models for recommendation. arXiv."},{"key":"ref_8","unstructured":"Guo, N., Cheng, H., Liang, Q., Chen, L., and Han, B. (2024). Integrating Large Language Models with Graphical Session-Based Recommendation. arXiv."},{"key":"ref_9","unstructured":"Du, Y., Wang, Z., Sun, Z., Chua, H., Liu, H., Wu, Z., Ma, Y., Zhang, J., and Sun, Y. (2024). Large Language Model with Graph Convolution for Recommendation. arXiv."},{"key":"ref_10","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, NeurIPS."},{"key":"ref_11","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 (ICDM), Singapore.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"ref_12","unstructured":"Luo, S., Yao, Y., He, B., Huang, Y., Zhou, A., Zhang, X., Xiao, Y., Zhan, M., and Song, L. (2024). Integrating large language models into recommendation via mutual augmentation and adaptive aggregation. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Bao, K., Yan, M., Wang, W., Feng, F., and He, X. (2024). Text-like Encoding of Collaborative Information in Large Language Models for Recommendation. arXiv.","DOI":"10.18653\/v1\/2024.acl-long.497"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., and He, X. (2023, January 18\u201322). Tallrec: An effective and efficient tuning framework to align large language model with recommendation. Proceedings of the 17th ACM Conference on Recommender Systems, Singapore.","DOI":"10.1145\/3604915.3608857"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Geng, S., Liu, S., Fu, Z., Ge, Y., and Zhang, Y. (2022, January 18\u201323). Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). Proceedings of the 16th ACM Conference on Recommender Systems, New York, NY, USA.","DOI":"10.1145\/3523227.3546767"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, J., Gao, C., Yuan, S., Liu, S., Cai, Q., and Jiang, P. (2025, January 10\u201314). Dlcrec: A novel approach for managing diversity in llm-based recommender systems. Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, Hannover, Germany.","DOI":"10.1145\/3701551.3703572"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cao, Y., Mehta, N., Yi, X., Keshavan, R., Heldt, L., Hong, L., Chi, E.H., and Sathiamoorthy, M. (2024). Aligning Large Language Models with Recommendation Knowledge. arXiv.","DOI":"10.18653\/v1\/2024.findings-naacl.67"},{"key":"ref_18","unstructured":"Xu, W., Wu, Q., Liang, Z., Han, J., Ning, X., Shi, Y., Lin, W., and Zhang, Y. (2025). SLMRec: Distilling large language models into small for sequential recommendation. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ji, J., Li, Z., Xu, S., Hua, W., Ge, Y., Tan, J., and Zhang, Y. (2024). Genrec: Large language model for generative recommendation. European Conference on Information Retrieval, Springer Nature.","DOI":"10.1007\/978-3-031-56063-7_42"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, S., Ma, W., Sun, P., Zhang, M., Ai, Q., Liu, Y., and Cai, M. (2024). Common sense enhanced knowledge-based recommendation with large language model. International Conference on Database Systems for Advanced Applications, Springer Nature.","DOI":"10.1007\/978-981-97-5569-1_25"},{"key":"ref_21","unstructured":"Zheng, B., Wang, X., Liu, E., Wang, X., Hongyu, L., Chen, Y., Zhao, W.X., and Wen, J.R. (2025). DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation. arXiv."},{"key":"ref_22","unstructured":"Luo, W., Song, C., Yi, L., and Cheng, G. (2024). TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance. arXiv."},{"key":"ref_23","unstructured":"Fang, Y., Wang, W., Zhang, Y., Zhu, F., Wang, Q., Feng, F., and He, X. (2025). Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment. arXiv."},{"key":"ref_24","unstructured":"Wang, L., Hu, H., Sha, L., Xu, C., Wong, K.F., and Jiang, D. (2021). RecInDial: A unified framework for conversational recommendation with pretrained language models. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ren, X., Wei, W., Xia, L., Su, L., Cheng, S., Wang, J., Yin, D., and Huang, C. (2024, January 13\u201317). Representation learning with large language models for recommendation. Proceedings of the ACM on Web Conference 2024, Singapore.","DOI":"10.1145\/3589334.3645458"},{"key":"ref_26","unstructured":"Liu, J., Liu, C., Zhou, P., Lv, R., Zhou, K., and Zhang, Y. (2023). Is chatgpt a good recommender? A preliminary study. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Chao, W., Qiu, Z., Zhu, H., and Xiong, H. (2024, January 13\u201317). Harnessing large language models for text-rich sequential recommendation. Proceedings of the ACM on Web Conference 2024, Singapore.","DOI":"10.1145\/3589334.3645358"},{"key":"ref_28","first-page":"1","article-title":"The MovieLens Datasets: History and Context","volume":"5","author":"Harper","year":"2015","journal-title":"ACM  Trans. Interact. Intell. Syst. (TiiS)"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"McAuley, J., Targett, C., Shi, Q., and van den Hengel, A. (2015, January 9\u201313). Image-based recommendations on styles and substitutes. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR \u201915), Santiago, Chile.","DOI":"10.1145\/2766462.2767755"},{"key":"ref_30","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Yang, Z., He, X., Zhang, J., Wu, J., Xin, X., Chen, J., and Wang, X. (2023, January 23\u201327). A generic learning framework for sequential recommendation with distribution shifts. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan.","DOI":"10.1145\/3539618.3591624"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"McAuley, J., and Leskovec, J. (2013, January 12\u201316). Hidden factors and hidden topics: Understanding rating dimensions with review text. Proceedings of the 7th ACM Conference on Recommender Systems, Hong Kong, China.","DOI":"10.1145\/2507157.2507163"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, P., Li, J., and Li, G. (2019, January 10\u201312). Research on Collaborative Filtering Recommendation Algorithm Based on Sentiment Analysis and Topic Model. Proceedings of the 4th International Conference on Big Data and Computing, Guangzhou, China.","DOI":"10.1145\/3335484.3335536"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zheng, L., Noroozi, V., and Yu, P.S. (2017, January 6\u201310). Joint deep modeling of users and items using reviews for recommendation. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, UK.","DOI":"10.1145\/3018661.3018665"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhang, M., Liu, Y., and Ma, S. (2018, January 23\u201327). Neural attentional rating regression with review-level explanations. Proceedings of the 2018 World Wide Web Conference, Lyon, France.","DOI":"10.1145\/3178876.3186070"},{"key":"ref_37","first-page":"43","article-title":"A Deep Recommendation Model with Multi-Layer Interaction and Sentiment Analysis","volume":"7","author":"Li","year":"2023","journal-title":"Data Anal. Knowl. Discov."},{"key":"ref_38","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022). LoRA: Low-Rank Adaptation of Large Language Models. arXiv."},{"key":"ref_39","unstructured":"Loshchilov, I., and Hutter, F. (2016). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_40","unstructured":"Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., Zhu, X., and Gai, K. (February, January 27). Deep Interest Evolution Network for Click-Through Rate Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_41","unstructured":"Li, W., Zhu, X., Zhang, B., Geng, L., and Ji, J. (2025, January 4\u20136). Time Series Large Language Model for Recommendation System. Proceedings of the 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), Chengdu, China."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/9\/818\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:46:40Z","timestamp":1760035600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/9\/818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["info16090818"],"URL":"https:\/\/doi.org\/10.3390\/info16090818","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,9,21]]}}}