{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T06:46:41Z","timestamp":1778568401746,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,8]]},"DOI":"10.1145\/3640457.3688023","type":"proceedings-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T11:39:28Z","timestamp":1728387568000},"page":"1361-1367","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2880-6715","authenticated-orcid":false,"given":"Alessandro","family":"Petruzzelli","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo moro, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2024. ChipNeMo: Domain-Adapted LLMs for Chip Design. arxiv:2311.00176\u00a0[cs.CL]"},{"key":"e_1_3_2_1_2_1","unstructured":"Tom\u00a0B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel\u00a0M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arxiv:2005.14165\u00a0[cs.CL]"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2533670.2533675"},{"key":"e_1_3_2_1_4_1","unstructured":"Aakanksha Chowdhery Sharan Narang Jacob Devlin Maarten Bosma Gaurav Mishra Adam Roberts Paul Barham Hyung\u00a0Won Chung Charles Sutton Sebastian Gehrmann Parker Schuh Kensen Shi Sasha Tsvyashchenko Joshua Maynez Abhishek Rao Parker Barnes Yi Tay Noam Shazeer Vinodkumar Prabhakaran Emily Reif Nan Du Ben Hutchinson Reiner Pope James Bradbury Jacob Austin Michael Isard Guy Gur-Ari Pengcheng Yin Toju Duke Anselm Levskaya Sanjay Ghemawat Sunipa Dev Henryk Michalewski Xavier Garcia Vedant Misra Kevin Robinson Liam Fedus Denny Zhou Daphne Ippolito David Luan Hyeontaek Lim Barret Zoph Alexander Spiridonov Ryan Sepassi David Dohan Shivani Agrawal Mark Omernick Andrew\u00a0M. Dai Thanumalayan\u00a0Sankaranarayana Pillai Marie Pellat Aitor Lewkowycz Erica Moreira Rewon Child Oleksandr Polozov Katherine Lee Zongwei Zhou Xuezhi Wang Brennan Saeta Mark Diaz Orhan Firat Michele Catasta Jason Wei Kathy Meier-Hellstern Douglas Eck Jeff Dean Slav Petrov and Noah Fiedel. 2022. PaLM: Scaling Language Modeling with Pathways. arxiv:2204.02311\u00a0[cs.CL]"},{"key":"e_1_3_2_1_5_1","unstructured":"Hyung\u00a0Won Chung Le Hou Shayne Longpre Barret Zoph Yi Tay William Fedus Yunxuan Li Xuezhi Wang Mostafa Dehghani Siddhartha Brahma Albert Webson Shixiang\u00a0Shane Gu Zhuyun Dai Mirac Suzgun Xinyun Chen Aakanksha Chowdhery Alex Castro-Ros Marie Pellat Kevin Robinson Dasha Valter Sharan Narang Gaurav Mishra Adams Yu Vincent Zhao Yanping Huang Andrew Dai Hongkun Yu Slav Petrov Ed\u00a0H. Chi Jeff Dean Jacob Devlin Adam Roberts Denny Zhou Quoc\u00a0V. Le and Jason Wei. 2022. Scaling Instruction-Finetuned Language Models. arxiv:2210.11416\u00a0[cs.LG]"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.23919\/CNSM59352.2023.10327837"},{"key":"e_1_3_2_1_7_1","volume-title":"A Large Language Model Enhanced Conversational Recommender System. arXiv preprint arXiv:2308.06212","author":"Feng Yue","year":"2023","unstructured":"Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, and Fei Sun. 2023. A Large Language Model Enhanced Conversational Recommender System. arXiv preprint arXiv:2308.06212 (2023)."},{"key":"e_1_3_2_1_8_1","volume-title":"Leveraging Large Language Models in Conversational Recommender Systems. arXiv preprint arXiv:2305.07961","author":"Friedman Luke","year":"2023","unstructured":"Luke Friedman, Sameer Ahuja, David Allen, Terry Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, 2023. Leveraging Large Language Models in Conversational Recommender Systems. arXiv preprint arXiv:2305.07961 (2023)."},{"key":"e_1_3_2_1_9_1","unstructured":"Ivar Frisch and Mario Giulianelli. 2024. LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models. arxiv:2402.02896\u00a0[cs.CL]"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546767"},{"key":"e_1_3_2_1_11_1","unstructured":"Prem Gopalan Jake\u00a0M Hofman and David\u00a0M Blei. 2015. Scalable Recommendation with Hierarchical Poisson Factorization.. In UAI. 326\u2013335."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_13_1","unstructured":"Neil Houlsby Andrei Giurgiu Stanislaw Jastrzebski Bruna Morrone Quentin de Laroussilhe Andrea Gesmundo Mona Attariyan and Sylvain Gelly. 2019. Parameter-Efficient Transfer Learning for NLP. arxiv:1902.00751\u00a0[cs.LG]"},{"key":"e_1_3_2_1_14_1","unstructured":"Edward\u00a0J. 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\u00a0[cs.CL]"},{"key":"e_1_3_2_1_15_1","unstructured":"Zhiqiang Hu Lei Wang Yihuai Lan Wanyu Xu Ee-Peng Lim Lidong Bing Xing Xu Soujanya Poria and Roy Ka-Wei Lee. 2023. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models. arxiv:2304.01933\u00a0[cs.CL]"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113250"},{"key":"e_1_3_2_1_17_1","unstructured":"D. Jannach and Ahtsham Manzoor. 2020. End-to-End Learning for Conversational Recommendation: A Long Way to Go?. In IntRS@RecSys. https:\/\/api.semanticscholar.org\/CorpusID:225063127"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453154"},{"key":"e_1_3_2_1_19_1","volume-title":"Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons 61, 4","author":"Jarrahi Mohammad\u00a0Hossein","year":"2018","unstructured":"Mohammad\u00a0Hossein Jarrahi. 2018. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons 61, 4 (2018), 577\u2013586."},{"key":"e_1_3_2_1_20_1","volume-title":"Instruction-tuned Language Models are Better Knowledge Learners. arXiv preprint arXiv:2402.12847","author":"Jiang Zhengbao","year":"2024","unstructured":"Zhengbao Jiang, Zhiqing Sun, Weijia Shi, Pedro Rodriguez, Chunting Zhou, Graham Neubig, Xi\u00a0Victoria Lin, Wen-tau Yih, and Srinivasan Iyer. 2024. Instruction-tuned Language Models are Better Knowledge Learners. arXiv preprint arXiv:2402.12847 (2024)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.307"},{"key":"e_1_3_2_1_22_1","volume-title":"Literature survey: domain adaptation algorithms for natural language processing","author":"Qi Li.","year":"2012","unstructured":"Qi Li. 2012. Literature survey: domain adaptation algorithms for natural language processing. Department of Computer Science The Graduate Center, The City University of New York (2012), 8\u201310."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327546.3327641"},{"key":"e_1_3_2_1_24_1","volume-title":"Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190","author":"Li Xiang\u00a0Lisa","year":"2021","unstructured":"Xiang\u00a0Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)."},{"key":"e_1_3_2_1_25_1","unstructured":"Vladislav Lialin Namrata Shivagunde Sherin Muckatira and Anna Rumshisky. 2023. ReLoRA: High-Rank Training Through Low-Rank Updates. arxiv:2307.05695"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_1_27_1","unstructured":"Jiayi Liao Sihang Li Zhengyi Yang Jiancan Wu Yancheng Yuan and Xiang Wang. 2023. LLaRA: Aligning Large Language Models with Sequential Recommenders. arxiv:2312.02445\u00a0[cs.IR]"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Xiao Liu Kaixuan Ji Yicheng Fu Weng\u00a0Lam Tam Zhengxiao Du Zhilin Yang and Jie Tang. 2022. P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks. arxiv:2110.07602\u00a0[cs.CL]","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"e_1_3_2_1_29_1","volume-title":"Wen tau Yih, and Madian Khabsa","author":"Mao Yuning","year":"2022","unstructured":"Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Wen tau Yih, and Madian Khabsa. 2022. UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning. arxiv:2110.07577\u00a0[cs.CL]"},{"key":"e_1_3_2_1_30_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774\u00a0[cs.CL]"},{"key":"e_1_3_2_1_31_1","volume-title":"Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36","author":"Rafailov Rafael","year":"2024","unstructured":"Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher\u00a0D Manning, Stefano Ermon, and Chelsea Finn. 2024. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_32_1","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter\u00a0J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21, 140 (2020), 1\u201367.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI \u201909). AUAI Press, Arlington, Virginia, USA, 452\u2013461."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Andreas R\u00fcckl\u00e9 Gregor Geigle Max Glockner Tilman Beck Jonas Pfeiffer Nils Reimers and Iryna Gurevych. 2021. AdapterDrop: On the Efficiency of Adapters in Transformers. arxiv:2010.11918\u00a0[cs.LG]","DOI":"10.18653\/v1\/2021.emnlp-main.626"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.10.050"},{"key":"e_1_3_2_1_36_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371831"},{"key":"e_1_3_2_1_38_1","unstructured":"Hao Sun. 2023. Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF Prompting and Beyond. arxiv:2310.06147\u00a0[cs.LG]"},{"key":"e_1_3_2_1_39_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 Aurelien Rodriguez Armand Joulin Edouard Grave and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. arxiv:2302.13971\u00a0[cs.CL]"},{"key":"e_1_3_2_1_40_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian\u00a0Canton Ferrer Moya Chen Guillem Cucurull David Esiobu Jude Fernandes Jeremy Fu Wenyin Fu Brian Fuller Cynthia Gao Vedanuj Goswami Naman Goyal Anthony Hartshorn Saghar Hosseini Rui Hou Hakan Inan Marcin Kardas Viktor Kerkez Madian Khabsa Isabel Kloumann Artem Korenev Punit\u00a0Singh Koura Marie-Anne Lachaux Thibaut Lavril Jenya Lee Diana Liskovich Yinghai Lu Yuning Mao Xavier Martinet Todor Mihaylov Pushkar Mishra Igor Molybog Yixin Nie Andrew Poulton Jeremy Reizenstein Rashi Rungta Kalyan Saladi Alan Schelten Ruan Silva Eric\u00a0Michael Smith Ranjan Subramanian Xiaoqing\u00a0Ellen Tan Binh Tang Ross Taylor Adina Williams Jian\u00a0Xiang Kuan Puxin Xu Zheng Yan Iliyan Zarov Yuchen Zhang Angela Fan Melanie Kambadur Sharan Narang Aurelien Rodriguez Robert Stojnic Sergey Edunov and Thomas Scialom. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. arxiv:2307.09288\u00a0[cs.CL]"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441759"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_3_2_1_43_1","volume-title":"Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review. arXiv preprint arXiv:2402.18590","author":"Vats Arpita","year":"2024","unstructured":"Arpita Vats, Vinija Jain, Rahul Raja, and Aman Chadha. 2024. Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review. arXiv preprint arXiv:2402.18590 (2024)."},{"key":"e_1_3_2_1_44_1","volume-title":"Efficient large language models: A survey. arXiv preprint arXiv:2312.03863 1","author":"Wan Zhongwei","year":"2023","unstructured":"Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, 2023. Efficient large language models: A survey. arXiv preprint arXiv:2312.03863 1 (2023)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3397321"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539382"},{"key":"e_1_3_2_1_47_1","volume-title":"Recmind: Large language model powered agent for recommendation. arXiv preprint arXiv:2308.14296","author":"Wang Yancheng","year":"2023","unstructured":"Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, and Yingzhen Yang. 2023. Recmind: Large language model powered agent for recommendation. arXiv preprint arXiv:2308.14296 (2023)."},{"key":"e_1_3_2_1_48_1","volume-title":"Proceedings of the 4th Workshop on Knowledge and Reasoning in Practical Dialogue Systems.","author":"W\u00e4rnest\u00e5l Pontus","year":"2005","unstructured":"Pontus W\u00e4rnest\u00e5l. 2005. User evaluation of a conversational recommender system. In Proceedings of the 4th Workshop on Knowledge and Reasoning in Practical Dialogue Systems."},{"key":"e_1_3_2_1_49_1","unstructured":"Jason Wei Maarten Bosma Vincent\u00a0Y. Zhao Kelvin Guu Adams\u00a0Wei Yu Brian Lester Nan Du Andrew\u00a0M. Dai and Quoc\u00a0V. Le. 2022. Finetuned Language Models Are Zero-Shot Learners. arxiv:2109.01652\u00a0[cs.CL]"},{"key":"e_1_3_2_1_50_1","volume-title":"Dong Wang, and Even Oldridge.","author":"Yue Zhenrui","year":"2023","unstructured":"Zhenrui Yue, Sara Rabhi, Gabriel de Souza\u00a0Pereira Moreira, Dong Wang, and Even Oldridge. 2023. LlamaRec: Two-stage recommendation using large language models for ranking. arXiv preprint arXiv:2311.02089 (2023)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403143"},{"key":"e_1_3_2_1_52_1","volume-title":"Toolqa: A dataset for llm question answering with external tools. Advances in Neural Information Processing Systems 36","author":"Zhuang Yuchen","year":"2024","unstructured":"Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang. 2024. Toolqa: A dataset for llm question answering with external tools. Advances in Neural Information Processing Systems 36 (2024)."}],"event":{"name":"RecSys '24: 18th ACM Conference on Recommender Systems","location":"Bari Italy","acronym":"RecSys '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["18th ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640457.3688023","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3640457.3688023","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T19:48:06Z","timestamp":1767815286000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640457.3688023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":52,"alternative-id":["10.1145\/3640457.3688023","10.1145\/3640457"],"URL":"https:\/\/doi.org\/10.1145\/3640457.3688023","relation":{},"subject":[],"published":{"date-parts":[[2024,10,8]]},"assertion":[{"value":"2024-10-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}