{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:14:37Z","timestamp":1785420877176,"version":"3.56.0"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272349"],"award-info":[{"award-number":["62272349"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n                    Sequential recommendation systems aim to predict users\u2019 next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing data from other domains, known as multi-domain methods, is useful for alleviating these problems. However, traditional multi-domain methods rely on meaningless ID-based item representation, which makes it difficult to align items with similar meanings from different domains, yielding sup-optimal knowledge transfer. This article introduces\n                    <jats:sc>LLM-Rec<\/jats:sc>\n                    , a framework that utilizes pre-trained Large Language Models (LLMs) for domain-agnostic recommendation. Specifically, we mix user\u2019s behaviors from multiple domains and concatenate item titles into a sentence, then use LLMs for generating user and item representations. By mixing behaviors across different domains, we can exploit the knowledge encoded in LLMs to bridge the semantic across over multi-domain behaviors, thus obtaining semantically rich representations and improving performance in all domains. Furthermore, we explore the underlying reasons why LLMs are effective and investigate whether LLMs can understand the semantic correlations as the recommendation model, and if advanced techniques like scaling laws in NLP also work in recommendations. We conduct extensive experiments with LLMs ranging from 40 M to 6.7 B to answer the above questions and to verify the effectiveness of\n                    <jats:sc>LLM-Rec<\/jats:sc>\n                    in multi-domain recommendation. The source code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/WHUIR\/LLMRec\">https:\/\/github.com\/WHUIR\/LLMRec<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3705727","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T12:19:41Z","timestamp":1732623581000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["One Model for All: Large Language Models Are Domain-Agnostic Recommendation Systems"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8335-1157","authenticated-orcid":false,"given":"Zuoli","family":"Tang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3611-0901","authenticated-orcid":false,"given":"Zhaoxin","family":"Huan","sequence":"additional","affiliation":[{"name":"Ant Group Co Ltd, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7840-3364","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8055-0245","authenticated-orcid":false,"given":"Xiaolu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ant Group Co Ltd, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6276-5031","authenticated-orcid":false,"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"Ant Group Co Ltd, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9719-4638","authenticated-orcid":false,"given":"Chilin","family":"Fu","sequence":"additional","affiliation":[{"name":"Ant Group Co Ltd, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6033-6102","authenticated-orcid":false,"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Ant Group Co Ltd, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6755-871X","authenticated-orcid":false,"given":"Lixin","family":"Zou","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-6374","authenticated-orcid":false,"given":"Chenliang","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557262"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512090"},{"key":"e_1_3_3_4_2","unstructured":"Hyung Won Chung Le Hou Shayne Longpre Barret Zoph Yi Tay William Fedus Eric Li Xuezhi Wang Mostafa Dehghani Siddhartha Brahma et al. 2022. Scaling instruction-finetuned language models. arXiv:2210.11416. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:2210.11416"},{"key":"e_1_3_3_5_2","first-page":"7480","volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Dehghani Mostafa","year":"2023","unstructured":"Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al. 2023. Scaling vision transformers to 22 billion parameters. In Proceedings of the International Conference on Machine Learning. PMLR, 7480\u20137512."},{"key":"e_1_3_3_6_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:1810.04805"},{"key":"e_1_3_3_7_2","unstructured":"Hao Ding Yifei Ma Anoop Deoras Yuyang Wang and Hao Wang. 2021. Zero-shot recommender systems. arXiv:2105.08318. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:2105.08318"},{"key":"e_1_3_3_8_2","unstructured":"Ning Ding Yujia Qin Guang Yang Fuchao Wei Zonghan Yang Yusheng Su Shengding Hu Yulin Chen Chi-Min Chan Weize Chen et al. 2022. Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models. arXiv:2203.06904. Retrieved from https:\/\/arxiv.org\/abs\/2203.06904"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591689"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467176"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_3_12_2","volume-title":"4th International Conference on Learning Representations.","author":"Hidasi Bal\u00e1zs","year":"2016","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In 4th International Conference on Learning Representations. ICLR."},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539381"},{"key":"e_1_3_3_14_2","unstructured":"Edward J. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv:2106.09685. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:2106.09685"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_3_16_2","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:1412.6980"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599519"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412012"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583366"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00236"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220007"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487331"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481941"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412236"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591778"},{"key":"e_1_3_3_29_2","first-page":"5998","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, 5998\u20136008."},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583331"},{"key":"e_1_3_3_32_2","doi-asserted-by":"crossref","unstructured":"Zheng Yuan Fajie Yuan Yu Song Youhua Li Junchen Fu Fei Yang Yunzhu Pan and Yongxin Ni. 2023. Where to go next for recommender systems? ID- vs. modality-based recommender models revisited. arXiv:2303.13835. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:2303.13835","DOI":"10.1145\/3539618.3591932"},{"key":"e_1_3_3_33_2","unstructured":"Susan Zhang Stephen Roller Naman Goyal Mikel Artetxe Moya Chen Shuohui Chen Christopher Dewan Mona Diab Xian Li Xi Victoria Lin et al. 2022. Opt: Open pre-trained transformer language models. arXiv:2205.01068. Retrieved from https:\/\/arxiv.org\/abs\/arXiv:2205.01068"},{"key":"e_1_3_3_34_2","first-page":"725","volume-title":"Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI \u201910)","author":"Zhang Yu","year":"2010","unstructured":"Yu Zhang, Bin Cao, and Dit-Yan Yeung. 2010. Multi-domain collaborative filtering. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI \u201910), 725\u2013732."},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358166"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548072"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411954"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705727","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:36:19Z","timestamp":1776890179000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9,30]]}},"alternative-id":["10.1145\/3705727"],"URL":"https:\/\/doi.org\/10.1145\/3705727","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,10]]},"assertion":[{"value":"2023-12-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}