{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:17:15Z","timestamp":1780391835825,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Cisco Faculty Research Award"},{"name":"Commonwealth Cyber Initiative Awards","award":["VV-1Q23-007, HV2Q23-003, VV-1Q24-011"],"award-info":[{"award-number":["VV-1Q23-007, HV2Q23-003, VV-1Q24-011"]}]},{"DOI":"10.13039\/501100006374","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2006844, IIS-2144209, IIS-2223769, CNS2154962, BCS-2228534"],"award-info":[{"award-number":["IIS-2006844, IIS-2144209, IIS-2223769, CNS2154962, BCS-2228534"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JP Morgan Chase Faculty Research Award"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645347","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"3162-3172","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["Collaborative Large Language Model for Recommender Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-2788","authenticated-orcid":false,"given":"Yaochen","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-7695","authenticated-orcid":false,"given":"Liang","family":"Wu","sequence":"additional","affiliation":[{"name":"LinkedIn Inc., Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0078-1533","authenticated-orcid":false,"given":"Qi","family":"Guo","sequence":"additional","affiliation":[{"name":"LinkedIn Inc., Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4595-4631","authenticated-orcid":false,"given":"Liangjie","family":"Hong","sequence":"additional","affiliation":[{"name":"LinkedIn Inc., Sunnyvale, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-817X","authenticated-orcid":false,"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511763113"},{"key":"e_1_3_2_2_2_1","volume-title":"Where to go next for recommender systems? ID vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835","author":"Yuan Zheng","year":"2023","unstructured":"Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. Where to go next for recommender systems? ID vs. modality-based recommender models revisited. arXiv preprint arXiv:2303.13835, 2023."},{"key":"e_1_3_2_2_3_1","volume-title":"NeurIPS","volume":"20","author":"Mnih Andriy","year":"2007","unstructured":"Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. In NeurIPS, volume 20, 2007."},{"key":"e_1_3_2_2_4_1","volume-title":"Starspace: Embed all the things! In AAAI","author":"Wu Ledell","year":"2018","unstructured":"Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. Starspace: Embed all the things! In AAAI, volume 32, 2018."},{"key":"e_1_3_2_2_5_1","first-page":"91","volume-title":"Recommender Systems Handbook","author":"Koren Yehuda","year":"2021","unstructured":"Yehuda Koren, Steffen Rendle, and Robert Bell. Advances in collaborative filtering. Recommender Systems Handbook, pages 91--142, 2021."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_3"},{"key":"e_1_3_2_2_7_1","volume-title":"SIGKDD, page 3638--3649","author":"Zhu Yaochen","year":"2023","unstructured":"Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. Pathspecific counterfactual fairness for recommender systems. In SIGKDD, page 3638--3649, 2023."},{"key":"e_1_3_2_2_8_1","volume-title":"A survey of large language models. arXiv preprint arXiv:2303.18223","author":"Zhao Wayne Xin","year":"2023","unstructured":"Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023."},{"key":"e_1_3_2_2_9_1","volume-title":"Improving language understanding by generative pre-training","author":"Radford Alec","year":"2018","unstructured":"Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455856"},{"key":"e_1_3_2_2_11_1","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et al. LlaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 2023."},{"key":"e_1_3_2_2_12_1","volume-title":"Emergent abilities of large language models. arXiv preprint arXiv:2206.07682","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022."},{"key":"e_1_3_2_2_13_1","volume-title":"Knowledge editing for large language models: A survey","author":"Wang Song","year":"2023","unstructured":"Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li. Knowledge editing for large language models: A survey, 2023."},{"key":"e_1_3_2_2_14_1","volume-title":"Recommender systems in the era of large language models (LLMs). arXiv preprint arXiv:2307.02046","author":"Fan Wenqi","year":"2023","unstructured":"Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, YiqiWang, Jiliang Tang, and Qing Li. Recommender systems in the era of large language models (LLMs). arXiv preprint arXiv:2307.02046, 2023."},{"key":"e_1_3_2_2_15_1","first-page":"625","volume-title":"WWW","author":"McAuley Julian","year":"2016","unstructured":"Julian McAuley and Alex Yang. Addressing complex and subjective productrelated queries with customer reviews. In WWW, pages 625--635, 2016."},{"issue":"5","key":"e_1_3_2_2_16_1","first-page":"5371","article-title":"Variational bandwidth auto-encoder for hybrid recommender systems","volume":"35","author":"Zhu Yaochen","year":"2022","unstructured":"Yaochen Zhu and Zhenzhong Chen. Variational bandwidth auto-encoder for hybrid recommender systems. IEEE TKDE, 35(5):5371--5385, 2022.","journal-title":"IEEE TKDE"},{"key":"e_1_3_2_2_17_1","volume-title":"M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084","author":"Cui Zeyu","year":"2022","unstructured":"Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084, 2022."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546767"},{"key":"e_1_3_2_2_19_1","volume-title":"ICML","author":"Qu Jiaxing","year":"2023","unstructured":"Jiaxing Qu, Yuxuan Richard Xie, and Elif Ertekin. A language-based recommendation system for material discovery. In ICML, 2023."},{"issue":"4","key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3580488","article-title":"Personalized prompt learning for explainable recommendation","volume":"41","author":"Li Lei","year":"2023","unstructured":"Lei Li, Yongfeng Zhang, and Li Chen. Personalized prompt learning for explainable recommendation. ACM TIST, 41(4):1--26, 2023.","journal-title":"ACM TIST"},{"key":"e_1_3_2_2_21_1","volume-title":"Chat-Rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524","author":"Gao Yunfan","year":"2023","unstructured":"Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. Chat-Rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524, 2023."},{"key":"e_1_3_2_2_22_1","volume-title":"Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845","author":"Hou Yupeng","year":"2023","unstructured":"Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845, 2023."},{"key":"e_1_3_2_2_23_1","volume-title":"Leyu Lin, and Ji-Rong Wen. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001","author":"Zhang Junjie","year":"2023","unstructured":"Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001, 2023."},{"key":"e_1_3_2_2_24_1","volume-title":"Nathan Kallus, and Julian McAuley. Large language models as zero-shot conversational recommenders. arXiv preprint arXiv:2308.10053","author":"He Zhankui","year":"2023","unstructured":"Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. Large language models as zero-shot conversational recommenders. arXiv preprint arXiv:2308.10053, 2023."},{"key":"e_1_3_2_2_25_1","volume-title":"Palr: Personalization aware llms for recommendation. arXiv e-prints","author":"Yang Fan","year":"2023","unstructured":"Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. Palr: Personalization aware llms for recommendation. arXiv e-prints, pages arXiv--2305, 2023."},{"key":"e_1_3_2_2_26_1","volume-title":"GenRec: Large language model for generative recommendation. arXiv e-prints","author":"Ji Jianchao","year":"2023","unstructured":"Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. GenRec: Large language model for generative recommendation. arXiv e-prints, pages arXiv--2307, 2023."},{"key":"e_1_3_2_2_27_1","volume-title":"Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837","author":"Chu Zhixuan","year":"2023","unstructured":"Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, et al. Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837, 2023."},{"key":"e_1_3_2_2_28_1","volume-title":"How to index item ids for recommendation foundation models. arXiv preprint arXiv:2305.06569","author":"Hua Wenyue","year":"2023","unstructured":"Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. How to index item ids for recommendation foundation models. arXiv preprint arXiv:2305.06569, 2023."},{"key":"e_1_3_2_2_29_1","volume-title":"The power of scale for parameterefficient prompt tuning. arXiv preprint arXiv:2104.08691","author":"Lester Brian","year":"2021","unstructured":"Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameterefficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021."},{"key":"e_1_3_2_2_30_1","volume-title":"NeurIPS","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, volume 30, 2017."},{"key":"e_1_3_2_2_31_1","first-page":"4171","volume-title":"NAACL","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: pretraining of deep bidirectional transformers for language understanding. In NAACL, pages 4171--4186, 2019."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3605943"},{"key":"e_1_3_2_2_33_1","volume-title":"prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735","author":"Liu Peng","year":"2023","unstructured":"Peng Liu, Lemei Zhang, and Jon Atle Gulla. Pre-train, prompt and recommendation: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735, 2023."},{"key":"e_1_3_2_2_34_1","volume-title":"How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817","author":"Lin Jianghao","year":"2023","unstructured":"Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817, 2023."},{"key":"e_1_3_2_2_35_1","volume-title":"TallRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447","author":"Bao Keqin","year":"2023","unstructured":"Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. TallRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447, 2023."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_2_37_1","volume-title":"Machine learning: A probabilistic perspective","author":"Murphy Kevin P","year":"2012","unstructured":"Kevin P Murphy. Machine learning: A probabilistic perspective. MIT press, 2012."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411954"},{"key":"e_1_3_2_2_39_1","first-page":"689","volume-title":"WWW","author":"Liang Dawen","year":"2018","unstructured":"Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Variational autoencoders for collaborative filtering. In WWW, pages 689--698, 2018."},{"key":"e_1_3_2_2_40_1","first-page":"2379","volume-title":"WWW","author":"Zhu Yaochen","year":"2022","unstructured":"Yaochen Zhu and Zhenzhong Chen. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW, pages 2379--2387, 2022."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_2_42_1","first-page":"3251","volume-title":"WWW","author":"Steck Harald","year":"2019","unstructured":"Harald Steck. Embarrassingly shallow autoencoders for sparse data. In WWW, pages 3251--3257, 2019."},{"key":"e_1_3_2_2_43_1","first-page":"452","volume-title":"UAI","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452--461, 2009."}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645347","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645347","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:23:03Z","timestamp":1755822183000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":43,"alternative-id":["10.1145\/3589334.3645347","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645347","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}