{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:11:18Z","timestamp":1743826278505,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887192","type":"print"},{"value":"9783031887208","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-88720-8_33","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:08:49Z","timestamp":1743768529000},"page":"204-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cooperative and\u00a0Competitive LLM-Based Multi-Agent Systems for\u00a0Recommendation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1652-0745","authenticated-orcid":false,"given":"Marco","family":"Valentini","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","unstructured":"Abdollahpouri, H., Burke, R.: Multistakeholder recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 647\u2013677. Springer, New York (2022). https:\/\/doi.org\/10.1007\/978-1-0716-2197-4_17","DOI":"10.1007\/978-1-0716-2197-4_17"},{"key":"33_CR2","doi-asserted-by":"publisher","unstructured":"Abdollahpouri, H., Burke, R., Mobasher, B.: Recommender systems as multistakeholder environments. In: Bielikov\u00e1, M., Herder, E., Cena, F., Desmarais, M.C. (eds.) Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP 2017, Bratislava, Slovakia, 09\u201312 July 2017, pp. 347\u2013348. ACM (2017). https:\/\/doi.org\/10.1145\/3079628.3079657","DOI":"10.1145\/3079628.3079657"},{"key":"33_CR3","doi-asserted-by":"publisher","unstructured":"Bhattacharya, M., Ostuni, V., Lamkhede, S.: Joint modeling of search and recommendations via an unified contextual recommender (unicorn). In: Noia, T.D., et al (eds.) Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, 14\u201318 October 2024, pp. 793\u2013795. ACM (2024). https:\/\/doi.org\/10.1145\/3640457.3688034","DOI":"10.1145\/3640457.3688034"},{"key":"33_CR4","doi-asserted-by":"publisher","unstructured":"Burke, R., Abdollahpouri, H., Malthouse, E.C., Thai, K.P., Zhang, Y.: Recommendation in multistakeholder environments. In: Bogers, T., Said, A., Brusilovsky, P., Tikk, D. (eds.) Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, 16\u201320 September 2019, pp. 566\u2013567. ACM (2019). https:\/\/doi.org\/10.1145\/3298689.3346973","DOI":"10.1145\/3298689.3346973"},{"issue":"4","key":"33_CR5","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/S11280-024-01276-1","volume":"27","author":"J Chen","year":"2024","unstructured":"Chen, J.: When large language models meet personalization: perspectives of challenges and opportunities. World Wide Web (WWW) 27(4), 42 (2024). https:\/\/doi.org\/10.1007\/S11280-024-01276-1","journal-title":"World Wide Web (WWW)"},{"key":"33_CR6","doi-asserted-by":"publisher","unstructured":"Dai, S., et al.: Uncovering chatgpt\u2019s capabilities in recommender systems. In: Zhang, J., et al. (eds.) Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, 18\u201322 September 2023, pp. 1126\u20131132. ACM (2023). https:\/\/doi.org\/10.1145\/3604915.3610646","DOI":"10.1145\/3604915.3610646"},{"key":"33_CR7","doi-asserted-by":"publisher","unstructured":"Deldjoo, Y., et al.: A review of modern recommender systems using generative models (Gen-RecSys). In: Baeza-Yates, R., Bonchi, F. (eds.) Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, 25\u201329 August 2024, pp. 6448\u20136458. ACM (2024). https:\/\/doi.org\/10.1145\/3637528.3671474","DOI":"10.1145\/3637528.3671474"},{"key":"33_CR8","doi-asserted-by":"publisher","unstructured":"Di Palma, D.: Retrieval-augmented recommender system: enhancing recommender systems with large language models. In: Zhang, J., et al. (eds.) Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, 18\u201322 September 2023, pp. 1369\u20131373. ACM (2023). https:\/\/doi.org\/10.1145\/3604915.3608889","DOI":"10.1145\/3604915.3608889"},{"key":"33_CR9","unstructured":"Di Palma, D., et al.: Beyond words: can chatGPT support state-of-the-art recommender systems? In: IIR. CEUR Workshop Proceedings, vol.\u00a03802, pp. 13\u201322. CEUR-WS.org (2024)"},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"28573","DOI":"10.1109\/ACCESS.2018.2831228","volume":"6","author":"A Dorri","year":"2018","unstructured":"Dorri, A., Kanhere, S.S., Jurdak, R.: Multi-agent systems: a survey. IEEE Access 6, 28573\u201328593 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2831228","journal-title":"IEEE Access"},{"key":"33_CR11","doi-asserted-by":"publisher","unstructured":"Du, Y., Leibo, J.Z., Islam, U., Willis, R., Sunehag, P.: A review of cooperation in multi-agent learning. CoRR abs\/2312.05162 (2023). https:\/\/doi.org\/10.48550\/ARXIV.2312.05162","DOI":"10.48550\/ARXIV.2312.05162"},{"key":"33_CR12","doi-asserted-by":"publisher","unstructured":"Ferrara, A., et al.: DIVAN: deep-interest virality-aware network to exploit temporal dynamics in news recommendation. In: Proceedings of the Recommender Systems Challenge 2024, RecSysChallenge 2024, Bari, Italy, 14\u201318 October 2024, pp. 12\u201316. ACM (2024). https:\/\/doi.org\/10.1145\/3687151.3687153","DOI":"10.1145\/3687151.3687153"},{"key":"33_CR13","doi-asserted-by":"publisher","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: Golbeck, J., et al. (eds.) RecSys 2022: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, 18\u201323 September 2022, pp. 299\u2013315. ACM (2022). https:\/\/doi.org\/10.1145\/3523227.3546767","DOI":"10.1145\/3523227.3546767"},{"key":"33_CR14","unstructured":"Guo, T., et al.: Large language model based multi-agents: a survey of progress and challenges. In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI 2024, Jeju, South Korea, 3\u20139 August 2024, pp. 8048\u20138057. ijcai.org (2024). https:\/\/www.ijcai.org\/proceedings\/2024\/890"},{"key":"33_CR15","doi-asserted-by":"publisher","unstructured":"He, Z., et al.: Large language models as zero-shot conversational recommenders. In: Frommholz, I., et al. (eds.) Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, 21\u201325 October 2023, pp. 720\u2013730. ACM (2023). https:\/\/doi.org\/10.1145\/3583780.3614949","DOI":"10.1145\/3583780.3614949"},{"key":"33_CR16","doi-asserted-by":"publisher","unstructured":"Jannach, D., Manzoor, A., Cai, W., Chen, L.: A survey on conversational recommender systems. ACM Comput. Surv. 54(5), 105:1\u2013105:36 (2022). https:\/\/doi.org\/10.1145\/3453154","DOI":"10.1145\/3453154"},{"key":"33_CR17","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9 2022 (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/8bb0d291acd4acf06ef112099c16f326-Abstract-Conference.html"},{"key":"33_CR18","doi-asserted-by":"publisher","unstructured":"Lee, J.: InstructpatentGPT: training patent language models to follow instructions with human feedback. CoRR abs\/2406.16897 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2406.16897","DOI":"10.48550\/ARXIV.2406.16897"},{"key":"33_CR19","unstructured":"Li, G., Hammoud, H., Itani, H., Khizbullin, D., Ghanem, B.: CAMEL: communicative agents for \u201cmind\u201d exploration of large language model society. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, 10\u201316 December 2023 (2023). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/a3621ee907def47c1b952ade25c67698-Abstract-Conference.html"},{"key":"33_CR20","doi-asserted-by":"publisher","unstructured":"Lops, P., Silletti, A., Polignano, M., Musto, C., Semeraro, G.: Reproducibility of LLM-based recommender systems: the case study of P5 paradigm. In: Noia, T.D., et al. (eds.) Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, 14\u201318 October 2024, pp. 116\u2013125. ACM (2024). https:\/\/doi.org\/10.1145\/3640457.3688072","DOI":"10.1145\/3640457.3688072"},{"key":"33_CR21","doi-asserted-by":"publisher","unstructured":"Nie, G., et al.: A hybrid multi-agent conversational recommender system with LLM and search engine in e-commerce. In: Noia, T.D., et al. (eds.) Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, 14\u201318 October 2024, pp. 745\u2013747. ACM (2024). https:\/\/doi.org\/10.1145\/3640457.3688061","DOI":"10.1145\/3640457.3688061"},{"key":"33_CR22","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.ins.2022.07.169","volume":"614","author":"TD Noia","year":"2022","unstructured":"Noia, T.D., Donini, F.M., Jannach, D., Narducci, F., Pomo, C.: Conversational recommendation: theoretical model and complexity analysis. Inf. Sci. 614, 325\u2013347 (2022)","journal-title":"Inf. Sci."},{"key":"33_CR23","doi-asserted-by":"publisher","unstructured":"Ousidhoum, N., Zhao, X., Fang, T., Song, Y., Yeung, D.: Probing toxic content in large pre-trained language models. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, 1\u20136 August 2021, pp. 4262\u20134274. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/V1\/2021.ACL-LONG.329","DOI":"10.18653\/V1\/2021.ACL-LONG.329"},{"key":"33_CR24","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/b1efde53be364a73914f58805a001731-Abstract-Conference.html"},{"key":"33_CR25","doi-asserted-by":"publisher","unstructured":"Penha, G., Vardasbi, A., Palumbo, E., Nadai, M.D., Bouchard, H.: Bridging search and recommendation in generative retrieval: does one task help the other? In: Noia, T.D., et al. (eds.) Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, 14\u201318 October 2024, pp. 340\u2013349. ACM (2024). https:\/\/doi.org\/10.1145\/3640457.3688123","DOI":"10.1145\/3640457.3688123"},{"key":"33_CR26","doi-asserted-by":"publisher","unstructured":"Petrov, A.V., Macdonald, C.: Aligning GPTRec with beyond-accuracy goals with reinforcement learning. CoRR abs\/2403.04875 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2403.04875","DOI":"10.48550\/ARXIV.2403.04875"},{"key":"33_CR27","doi-asserted-by":"publisher","unstructured":"Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer, New York (2022). https:\/\/doi.org\/10.1007\/978-1-0716-2197-4","DOI":"10.1007\/978-1-0716-2197-4"},{"key":"33_CR28","unstructured":"Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 4th edn. Pearson (2020). http:\/\/aima.cs.berkeley.edu\/"},{"key":"33_CR29","doi-asserted-by":"publisher","unstructured":"Schwartz, R., Dodge, J., Smith, N.A., Etzioni, O.: Green AI. Commun. ACM 63(12), 54\u201363 (2020). https:\/\/doi.org\/10.1145\/3381831","DOI":"10.1145\/3381831"},{"issue":"2","key":"33_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/J.JJIMEI.2024.100261","volume":"4","author":"L Shahrzadi","year":"2024","unstructured":"Shahrzadi, L., Mansouri, A., Alavi, M., Shabani, A.: Causes, consequences, and strategies to deal with information overload: a scoping review. Int. J. Inf. Manag. Data Insights 4(2), 100261 (2024). https:\/\/doi.org\/10.1016\/J.JJIMEI.2024.100261","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"33_CR31","doi-asserted-by":"publisher","unstructured":"Tsai, A., et al.: Leveraging LLM reasoning enhances personalized recommender systems. In: Ku, L., Martins, A., Srikumar, V. (eds.) Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and Virtual Meeting, 11\u201316 August 2024, pp. 13176\u201313188. Association for Computational Linguistics (2024). https:\/\/doi.org\/10.18653\/V1\/2024.FINDINGS-ACL.780","DOI":"10.18653\/V1\/2024.FINDINGS-ACL.780"},{"issue":"5","key":"33_CR32","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1109\/JAS.2022.105506","volume":"9","author":"J Wang","year":"2022","unstructured":"Wang, J., et al.: Cooperative and competitive multi-agent systems: from optimization to games. IEEE CAA J. Autom. Sinica 9(5), 763\u2013783 (2022). https:\/\/doi.org\/10.1109\/JAS.2022.105506","journal-title":"IEEE CAA J. Autom. Sinica"},{"key":"33_CR33","doi-asserted-by":"publisher","unstructured":"Wang, Z., Yu, Y., Zheng, W., Ma, W., Zhang, M.: MACRec: a multi-agent collaboration framework for recommendation. In: Yang, G.H., Wang, H., Han, S., Hauff, C., Zuccon, G., Zhang, Y. (eds.) Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, 14\u201318 July 2024, pp. 2760\u20132764. ACM (2024). https:\/\/doi.org\/10.1145\/3626772.3657669","DOI":"10.1145\/3626772.3657669"},{"issue":"5","key":"33_CR34","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/S11280-024-01291-2","volume":"27","author":"L Wu","year":"2024","unstructured":"Wu, L., et al.: A survey on large language models for recommendation. World Wide Web (WWW) 27(5), 60 (2024). https:\/\/doi.org\/10.1007\/S11280-024-01291-2","journal-title":"World Wide Web (WWW)"},{"key":"33_CR35","doi-asserted-by":"publisher","unstructured":"Xu, L., et al.: Prompting large language models for recommender systems: a comprehensive framework and empirical analysis. CoRR abs\/2401.04997 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2401.04997","DOI":"10.48550\/ARXIV.2401.04997"},{"key":"33_CR36","doi-asserted-by":"publisher","unstructured":"Yang, T., Chen, L.: Unleashing the retrieval potential of large language models in conversational recommender systems. In: Noia, T.D., et al. (eds.) Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, 14\u201318 October 2024, pp. 43\u201352. ACM (2024). https:\/\/doi.org\/10.1145\/3640457.3688146","DOI":"10.1145\/3640457.3688146"},{"key":"33_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, A., Chen, Y., Sheng, L., Wang, X., Chua, T.: On generative agents in recommendation. In: Yang, G.H., Wang, H., Han, S., Hauff, C., Zuccon, G., Zhang, Y. (eds.) Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, 14\u201318 July 2024, pp. 1807\u20131817. ACM (2024). https:\/\/doi.org\/10.1145\/3626772.3657844","DOI":"10.1145\/3626772.3657844"},{"key":"33_CR38","doi-asserted-by":"publisher","unstructured":"Zhang, G.: User-centric conversational recommendation: adapting the need of user with large language models. In: Zhang, J., et al. (eds.) Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, 18\u201322 September 2023, pp. 1349\u20131354. ACM (2023). https:\/\/doi.org\/10.1145\/3604915.3608885","DOI":"10.1145\/3604915.3608885"},{"key":"33_CR39","doi-asserted-by":"publisher","unstructured":"Zhang, J., Bao, K., Zhang, Y., Wang, W., Feng, F., He, X.: Large language models for recommendation: progresses and future directions. In: Chua, T., Ngo, C., Lee, R.K., Kumar, R., Lauw, H.W. (eds.) Companion Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, Singapore, 13\u201317 May 2024, pp. 1268\u20131271. ACM (2024). https:\/\/doi.org\/10.1145\/3589335.3641247","DOI":"10.1145\/3589335.3641247"},{"key":"33_CR40","doi-asserted-by":"publisher","unstructured":"Zhang, N., et al.: A comprehensive study of knowledge editing for large language models. CoRR abs\/2401.01286 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2401.01286","DOI":"10.48550\/ARXIV.2401.01286"},{"key":"33_CR41","unstructured":"Zhu, Y., et al.: Can large language models understand context? In: Graham, Y., Purver, M. (eds.) Findings of the Association for Computational Linguistics: EACL 2024, St. Julian\u2019s, Malta, 17\u201322 March 2024, pp. 2004\u20132018. Association for Computational Linguistics (2024). https:\/\/aclanthology.org\/2024.findings-eacl.135"}],"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\/978-3-031-88720-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:09:11Z","timestamp":1743768551000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88720-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887192","9783031887208"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88720-8_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}