{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:53:38Z","timestamp":1774360418153,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032212993","type":"print"},{"value":"9783032213006","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-21300-6_42","type":"book-chapter","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:58:04Z","timestamp":1774357084000},"page":"508-517","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Nikita","family":"Severin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danil","family":"Kartushov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladislav","family":"Urzhumov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladislav","family":"Kulikov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oksana","family":"Konovalova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey","family":"Grishanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anton","family":"Klenitskiy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artem","family":"Fatkulin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey","family":"Vasilev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrey","family":"Savchenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilya","family":"Makarov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"42_CR1","unstructured":"Bao, K., et al.: A bi-step grounding paradigm for large language models in recommendation systems. arXiv preprint arXiv:2308.08434 (2023)"},{"key":"42_CR2","unstructured":"Chen, S., Li, X., Dong, J., Zhang, J., Wang, Y., Wang, X.: TBIN: modeling long textual behavior data for ctr prediction. arXiv preprint arXiv:2308.08483 (2023)"},{"key":"42_CR3","unstructured":"Gemma, T.: Gemma 2: Improving open language models at a practical size (2024). https:\/\/arxiv.org\/abs\/2408.00118"},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In: Proceedings of the 16th ACM Conference on Recommender Systems, pp. 299\u2013315 (2022)","DOI":"10.1145\/3523227.3546767"},{"key":"42_CR5","doi-asserted-by":"publisher","unstructured":"Gusak, D., Volodkevich, A., Klenitskiy, A., Vasilev, A., Frolov, E.: Time to split: exploring data splitting strategies for offline evaluation of sequential recommenders. In: Proceedings of the Nineteenth ACM Conference on Recommender Systems, RecSys 2025, pp. 874\u2013883. ACM (Sep 2025). https:\/\/doi.org\/10.1145\/3705328.3748164","DOI":"10.1145\/3705328.3748164"},{"key":"42_CR6","doi-asserted-by":"publisher","unstructured":"Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1\u201319:19 (2016). https:\/\/doi.org\/10.1145\/2827872","DOI":"10.1145\/2827872"},{"key":"42_CR7","unstructured":"Hou, Y., et al.: Llamarec: two-stage recommendation using large language models for ranking. arXiv preprint arXiv:2311.02089 (2023)"},{"key":"42_CR8","unstructured":"Hou, Y., et al.: Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845 (2024). https:\/\/arxiv.org\/abs\/2305.08845"},{"key":"42_CR9","doi-asserted-by":"publisher","unstructured":"Ji, Y., Sun, A., Zhang, J., Li, C.: A critical study on data leakage in recommender system offline evaluation. ACM Trans. Inf. Syst. 41(3) (2023).https:\/\/doi.org\/10.1145\/3569930, https:\/\/doi.org\/10.1145\/3569930","DOI":"10.1145\/3569930"},{"key":"42_CR10","unstructured":"Jin, W., et al.: Amazon-M2: A multilingual multi-locale shopping session dataset for recommendation and text generation (2023). https:\/\/arxiv.org\/abs\/2307.09688"},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), pp. 197\u2013206. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation (2018). https:\/\/arxiv.org\/abs\/1808.09781","DOI":"10.1109\/ICDM.2018.00035"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Klenitskiy, A., Vasilev, A.: Turning dross into gold loss: is BERT4Rec really better than SASRec? In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1120\u20131125 (2023)","DOI":"10.1145\/3604915.3610644"},{"key":"42_CR14","doi-asserted-by":"publisher","unstructured":"Klenitskiy, A., Volodkevich, A., Pembek, A., Vasilev, A.: Does it look sequential? an analysis of datasets for evaluation of sequential recommendations. In: Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, pp. 1067\u20131072. Association for Computing Machinery, New York (2024).https:\/\/doi.org\/10.1145\/3640457.3688195","DOI":"10.1145\/3640457.3688195"},{"key":"42_CR15","doi-asserted-by":"crossref","unstructured":"Koren, Y., Rendle, S., Bell, R.: Advances in collaborative filtering. Recommender Systems Handbook, pp. 91\u2013142 (2021)","DOI":"10.1007\/978-1-0716-2197-4_3"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269\u20131278 (2019)","DOI":"10.1145\/3292500.3330895"},{"key":"42_CR17","doi-asserted-by":"publisher","unstructured":"Lee, S., Choi, M., Choi, E., Kim, H.y., Lee, J.: GRAM: generative recommendation via semantic-aware multi-granular late fusion. In: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T. (eds.) Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 33294\u201333312. Association for Computational Linguistics, Vienna, Austria (Jul 2025). https:\/\/doi.org\/10.18653\/v1\/2025.acl-long.1596, https:\/\/aclanthology.org\/2025.acl-long.1596\/","DOI":"10.18653\/v1\/2025.acl-long.1596"},{"key":"42_CR18","unstructured":"Li, X., Chen, C., Zhao, X., Zhang, Y., Xing, C.: E4SRec: an elegant effective efficient extensible solution of large language models for sequential recommendation (2023). https:\/\/arxiv.org\/abs\/2312.02443"},{"key":"42_CR19","doi-asserted-by":"publisher","unstructured":"Li, Y., Zhai, X., Alzantot, M., Yu, K., Vuli\u0107, I., Korhonen, A., Hammad, M.: CALRec: Contrastive alignment of generative llms for sequential recommendation. In: Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, pp. 422\u2013432. Association for Computing Machinery, New York (2024).https:\/\/doi.org\/10.1145\/3640457.3688121","DOI":"10.1145\/3640457.3688121"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Lin, G., Zhang, Y.: Sparks of artificial general recommender (AGR): Early experiments with ChatGPT. arXiv preprint arXiv:2305.04518 (2023)","DOI":"10.3390\/a16090432"},{"key":"42_CR21","doi-asserted-by":"publisher","unstructured":"Liu, Q., et al.: LLMEmb: large language model can be a good embedding generator for sequential recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39(11), pp. 12183\u201312191 (2025). https:\/\/doi.org\/10.1609\/aaai.v39i11.33327","DOI":"10.1609\/aaai.v39i11.33327"},{"key":"42_CR22","unstructured":"Liu, Q., Zhu, J., Dai, Q., Wu, X.M.: Boosting deep CTR prediction with a plug-and-play pre-trainer for news recommendation. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 2823\u20132833 (2022)"},{"key":"42_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Pre-trained language model for web-scale retrieval in Baidu search. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3365\u20133375 (2021)","DOI":"10.1145\/3447548.3467149"},{"key":"42_CR24","doi-asserted-by":"publisher","unstructured":"McAuley, J.J., Targett, C., Shi, Q., van\u00a0den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, 9-13 August 2015, pp. 43\u201352. ACM (2015). https:\/\/doi.org\/10.1145\/2766462.2767755","DOI":"10.1145\/2766462.2767755"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"key":"42_CR26","unstructured":"Petrov, A., Safilo, I., Tikhonovich, D., Ignatov, D.: MTS Kion implicit contextualised sequential dataset for movie recommendation. In: Proceedings of the ACM RecSys CARS Workshop 2022, 23 September 2022 Seattle, WA, USA (2022)"},{"issue":"140","key":"42_CR27","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"42_CR28","doi-asserted-by":"crossref","unstructured":"Severin, N., et\u00a0al.: LLM-KT: a versatile framework for knowledge transfer from large language models to collaborative filtering. In: International Conference on Data Mining Workshops (ICDMW), pp. 903\u2013906. IEEE (2024)","DOI":"10.1109\/ICDMW65004.2024.00125"},{"key":"42_CR29","doi-asserted-by":"crossref","unstructured":"Sun, F., Liet al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441\u20131450 (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"42_CR30","unstructured":"Sun, Z., et al.: Large language models enhanced collaborative filtering. arXiv preprint arXiv:2403.17688 (2024)"},{"key":"42_CR31","doi-asserted-by":"crossref","unstructured":"Tan, J., Xu, S., Hua, W., Ge, Y., Li, Z., Zhang, Y.: IDGenRec: LLM-RecSys alignment with textual id learning. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 355\u2013364 (2024)","DOI":"10.1145\/3626772.3657821"},{"key":"42_CR32","doi-asserted-by":"publisher","unstructured":"Tian, C., et al.: Reland: integrating large language models\u2019 insights into industrial recommenders via a controllable reasoning pool. In: Proceedings of the 18th ACM Conference on Recommender Systems (RecSys 2024). ACM, Bari, Italy (2024).https:\/\/doi.org\/10.1145\/3640457.3688131","DOI":"10.1145\/3640457.3688131"},{"key":"42_CR33","doi-asserted-by":"crossref","unstructured":"Tikhonovich, D., et al.: eSASRec: enhancing transformer-based recommendations in a modular fashion. In: Proceedings of the Nineteenth ACM Conference on Recommender Systems, pp. 1175\u20131180 (2025)","DOI":"10.1145\/3705328.3759317"},{"key":"42_CR34","unstructured":"Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., Wei, F.: Multilingual e5 text embeddings: a technical report. arXiv preprint arXiv:2402.05672 (2024)"},{"key":"42_CR35","unstructured":"Wang, Q., et al.: Towards next-generation LLM-based recommender systems: A survey and beyond. arXiv preprint arXiv:2410.19744 (2024)"},{"key":"42_CR36","unstructured":"Wei, J., et\u00a0al.: Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)"},{"key":"42_CR37","unstructured":"Xi, Y., et al.: Towards open-world recommendation with knowledge augmentation from large language models. arXiv preprint arXiv:2306.10933 (2023)"},{"key":"42_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: TwHIN-BERT: a socially-enriched pre-trained language model for multilingual tweet representations at twitter. arXiv preprint arXiv:2209.07562 (2022)","DOI":"10.1145\/3580305.3599921"},{"key":"42_CR39","unstructured":"Zhao, W.X., et\u00a0al.: A survey of large language models. arXiv preprint arXiv:2303.18223 (2023)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21300-6_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:58:26Z","timestamp":1774357106000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21300-6_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032212993","9783032213006"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21300-6_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"25 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delft","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2026.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}