{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:55:47Z","timestamp":1774360547688,"version":"3.50.1"},"publisher-location":"Cham","reference-count":58,"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_13","type":"book-chapter","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:01:35Z","timestamp":1774357295000},"page":"204-221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Conversational Recommendation with\u00a0Contextual Adaptation of\u00a0External Recommenders and\u00a0LLM-Based Reranking"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8112-3505","authenticated-orcid":false,"given":"Chuang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weida","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hengchang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"See-Kiong","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min-Yen","family":"Kan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haizhou","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Bizer, C., et al.: DBpedia - a crystallization point for the web of data. J. Web Seman. 7(3), 154\u2013165 (2009). https:\/\/doi.org\/10.1016\/j.websem.2009.07.002, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1570826809000225, the Web of Data","DOI":"10.1016\/j.websem.2009.07.002"},{"issue":"2","key":"13_CR2","first-page":"1","volume":"4","author":"D Carraro","year":"2024","unstructured":"Carraro, D., Bridge, D.: Enhancing recommendation diversity by re-ranking with large language models. ACM Trans. Recommender Syst. 4(2), 1\u201340 (2024)","journal-title":"ACM Trans. Recommender Syst."},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Chen, Q., et al.: Towards knowledge-based recommender dialog system. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1803\u20131813. Association for Computational Linguistics, Hong Kong, China (Nov 2019). https:\/\/doi.org\/10.18653\/v1\/D19-1189, https:\/\/aclanthology.org\/D19-1189","DOI":"10.18653\/v1\/D19-1189"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Chen, S., et al.: Controllable multi-objective re-ranking with policy hypernetworks. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3855\u20133864. KDD \u201923, Association for Computing Machinery, New York, USA (2023). https:\/\/doi.org\/10.1145\/3580305.3599796, https:\/\/doi.org\/10.1145\/3580305.3599796","DOI":"10.1145\/3580305.3599796"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Dai, S., et al.: Uncovering chatgpt\u2019s capabilities in recommender systems. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1126\u20131132. RecSys \u201923, Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3604915.3610646","DOI":"10.1145\/3604915.3610646"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Deng, Y., Zhang, W., Xu, W., Lei, W., Chua, T.S., Lam, W.: A unified multi-task learning framework for multi-goal conversational recommender systems. ACM Trans. Inf. Syst. 41(3) (2023). https:\/\/doi.org\/10.1145\/3570640","DOI":"10.1145\/3570640"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423, https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"13_CR8","unstructured":"Feng, Y., et al.: A large language model enhanced conversational recommender system (2023)"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Gao, C., Lei, W., He, X., de Rijke, M., Chua, T.S.: Advances and challenges in conversational recommender systems: a survey. AI Open 2, 100\u2013126 (2021). https:\/\/doi.org\/10.1016\/j.aiopen.2021.06.002, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666651021000164","DOI":"10.1016\/j.aiopen.2021.06.002"},{"key":"13_CR10","unstructured":"Gao, Y., Sheng, T., Xiang, Y., Xiong, Y., Wang, H., Zhang, J.: Chat-rec: towards interactive and explainable LLMs-augmented recommender system (2023). arXiv preprint arXiv:2303.14524"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Ge, Y., et\u00a0al.: OpenAGI: when LLM meets domain experts. Adv. Neural Inf. Process. Syst. 36 (2024)","DOI":"10.52202\/075280-0242"},{"key":"13_CR12","unstructured":"Grattafiori, A., et\u00a0al.: The Llama 3 herd of models (2024). https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Hayati, S.A., Kang, D., Zhu, Q., Shi, W., Yu, Z.: Inspired: toward sociable recommendation dialog systems. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8142\u20138152. Association for Computational Linguistics, Online (Nov 2020). https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.654","DOI":"10.18653\/v1\/2020.emnlp-main.654"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"He, Z., et al.: Large language models as zero-shot conversational recommenders (2023). arXiv preprint arXiv:2308.10053","DOI":"10.1145\/3583780.3614949"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"He, Z., et al.: Reindex-then-adapt: improving large language models for conversational recommendation (2024). arXiv preprint arXiv:2405.12119","DOI":"10.1145\/3701551.3703573"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Hou, Y., et al.: Large language models are zero-shot rankers for recommender systems (2024). https:\/\/arxiv.org\/abs\/2305.08845","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"13_CR17","unstructured":"Huang, X., Lian, J., Lei, Y., Yao, J., Lian, D., Xie, X.: Recommender AI agent: integrating large language models for interactive recommendations (2023). arXiv preprint arXiv:2308.16505"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Jannach, D., Manzoor, A., Cai, W., Chen, L.: A survey on conversational recommender systems. ACM Comput. Surv. 54(5) (2021). https:\/\/doi.org\/10.1145\/3453154","DOI":"10.1145\/3453154"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation (2018). http:\/\/arxiv.org\/abs\/1808.09781","DOI":"10.1109\/ICDM.2018.00035"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Kim, S., Kang, H., Choi, S., Kim, D., Yang, M., Park, C.: Large language models meet collaborative filtering: An efficient all-round LLm-based recommender system. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1395\u20131406. KDD \u201924, Association for Computing Machinery, New York, USA (2024). https:\/\/doi.org\/10.1145\/3637528.3671931","DOI":"10.1145\/3637528.3671931"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Lei, W., et al.: Estimation-action-reflection: towards deep interaction between conversational and recommender systems. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 304\u2013312. WSDM \u201920, Association for Computing Machinery, New York, USA (2020). https:\/\/doi.org\/10.1145\/3336191.3371769","DOI":"10.1145\/3336191.3371769"},{"key":"13_CR22","unstructured":"Li, C., Deng, Y., Hu, H., Kan, M.Y., Li, H.: Incorporating external knowledge and goal guidance for LLM-based conversational recommender systems (2024). https:\/\/arxiv.org\/abs\/2405.01868"},{"key":"13_CR23","unstructured":"Li, C., Hu, H., Zhang, Y., Kan, M.Y., Li, H.: A conversation is worth a thousand recommendations: a survey of holistic conversational recommender systems. In: KaRS Workshop at ACM RecSys \u201923. Singapore (2023). https:\/\/arxiv.org\/abs\/2309.07682v1"},{"key":"13_CR24","unstructured":"Li, R., Kahou, S., Schulz, H., Michalski, V., Charlin, L., Pal, C.: Towards deep conversational recommendations. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 9748\u20139758. NIPS\u201918, Curran Associates Inc., Red Hook, USA (2018)"},{"key":"13_CR25","unstructured":"Li, R., Kahou, S.E., Schulz, H., Michalski, V., Charlin, L., Pal, C.: Towards deep conversational recommendations. In: Advances in Neural Information Processing Systems 31 (NIPS 2018) (2018)"},{"key":"13_CR26","doi-asserted-by":"publisher","unstructured":"Li, S., Xie, R., Zhu, Y., Ao, X., Zhuang, F., He, Q.: User-centric conversational recommendation with multi-aspect user modeling. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 223\u2013233 (2022). https:\/\/doi.org\/10.1145\/3477495.3532074, http:\/\/arxiv.org\/abs\/2204.09263","DOI":"10.1145\/3477495.3532074"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Lin, A., Wang, J., Zhu, Z., Caverlee, J.: Quantifying and mitigating popularity bias in conversational recommender systems. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 1238\u20131247 (2022)","DOI":"10.1145\/3511808.3557423"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1053\u20131058. IEEE (2016)","DOI":"10.1109\/ICDM.2016.0135"},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Liu, Y., et al.: Conversational recommender system and large language model are made for each other in E-commerce pre-sales dialogue. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 9587\u20139605. Association for Computational Linguistics, Singapore (Dec 2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.643, https:\/\/aclanthology.org\/2023.findings-emnlp.643","DOI":"10.18653\/v1\/2023.findings-emnlp.643"},{"key":"13_CR30","doi-asserted-by":"publisher","unstructured":"Liu, Z., Wang, H., Niu, Z.Y., Wu, H., Che, W.: DuRecDial 2.0: a bilingual parallel corpus for conversational recommendation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 4335\u20134347. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.356, https:\/\/aclanthology.org\/2021.emnlp-main.356","DOI":"10.18653\/v1\/2021.emnlp-main.356"},{"key":"13_CR31","unstructured":"Palma, D.D., Biancofiore, G.M., Anelli, V.W., Narducci, F., Noia, T.D., Sciascio, E.D.: Evaluating ChatGPT as a recommender system: a rigorous approach (2023)"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Pei, C., et\u00a0al.: Personalized re-ranking for recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 3\u201311 (2019)","DOI":"10.1145\/3298689.3347000"},{"key":"13_CR33","doi-asserted-by":"publisher","unstructured":"Petruzzelli, A., et al.: Improving transformer-based sequential conversational recommendations through knowledge graph embeddings. In: Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 172\u2013182. UMAP \u201924, Association for Computing Machinery, New York, USA (2024). https:\/\/doi.org\/10.1145\/3627043.3659565","DOI":"10.1145\/3627043.3659565"},{"key":"13_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117539","volume":"203","author":"D Pramod","year":"2022","unstructured":"Pramod, D., Bafna, P.: Conversational recommender systems techniques, tools, acceptance, and adoption: a state of the art review. Expert Syst. Appl. 203, 117539 (2022)","journal-title":"Expert Syst. Appl."},{"key":"13_CR35","doi-asserted-by":"publisher","unstructured":"Sanner, S., Balog, K., Radlinski, F., Wedin, B., Dixon, L.: Large language models are competitive near cold-start recommenders for language- and item-based preferences. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 890\u2013896. RecSys \u201923, Association for Computing Machinery, New York, USA (2023). https:\/\/doi.org\/10.1145\/3604915.3608845","DOI":"10.1145\/3604915.3608845"},{"issue":"1","key":"13_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103139","volume":"60","author":"T Shen","year":"2023","unstructured":"Shen, T., Li, J., Bouadjenek, M.R., Mai, Z., Sanner, S.: Towards understanding and mitigating unintended biases in language model-driven conversational recommendation. Inf. Process. Manag. 60(1), 103139 (2023)","journal-title":"Inf. Process. Manag."},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. CoRR (2019). abs\/1904.06690. http:\/\/arxiv.org\/abs\/1904.06690","DOI":"10.1145\/3357384.3357895"},{"key":"13_CR38","unstructured":"Tay, Y., et al.: Transformer memory as a differentiable search index (2022). https:\/\/arxiv.org\/abs\/2202.06991"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Wang, J., Lin, D., Li, W.: A target-driven planning approach for goal-directed dialog systems. IEEE Trans. Neural Network Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3242071"},{"key":"13_CR40","unstructured":"Wang, K., Lu, Y., Santacroce, M., Gong, Y., Zhang, C., Shen, Y.: Adapting LLM agents through communication (2023). arXiv preprint arXiv:2310.01444"},{"key":"13_CR41","unstructured":"Wang, L., Hu, H., Sha, L., Xu, C., Wong, K., Jiang, D.: Finetuning large-scale pre-trained language models for conversational recommendation with knowledge graph. CoRR (2021). abs\/2110.07477, https:\/\/arxiv.org\/abs\/2110.07477"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., Orgun, M.: Sequential recommender systems: challenges, progress and prospects (2019). arXiv preprint arXiv:2001.04830","DOI":"10.24963\/ijcai.2019\/883"},{"key":"13_CR43","unstructured":"Wang, W., Lin, X., Feng, F., He, X., Chua, T.S.: Generative recommendation: towards next-generation recommender paradigm (2023). arXiv preprint arXiv:2304.03516"},{"key":"13_CR44","doi-asserted-by":"publisher","unstructured":"Wang, X., Tang, X., Zhao, X., Wang, J., Wen, J.R.: Rethinking the evaluation for conversational recommendation in the era of large language models. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 10052\u201310065. Association for Computational Linguistics, Singapore (Dec 2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.621","DOI":"10.18653\/v1\/2023.emnlp-main.621"},{"key":"13_CR45","doi-asserted-by":"publisher","unstructured":"Wang, X., Zhou, K., Wen, J.R., Zhao, W.X.: Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1929\u20131937. KDD \u201922, Association for Computing Machinery, New York, USA (2022). https:\/\/doi.org\/10.1145\/3534678.3539382","DOI":"10.1145\/3534678.3539382"},{"key":"13_CR46","unstructured":"Wu, Q., et al.: Autogen: Enabling next-gen LLM applications via multi-agent conversation framework (2023). arXiv preprint arXiv:2308.08155"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Xi, Y., et al.: MemoCRS: memory-enhanced sequential conversational recommender systems with large language models (2024). https:\/\/arxiv.org\/abs\/2407.04960","DOI":"10.1145\/3627673.3679599"},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"Yang, T., Chen, L.: Unleashing the retrieval potential of large language models in conversational recommender systems. In: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 43\u201352 (2024)","DOI":"10.1145\/3640457.3688146"},{"key":"13_CR49","unstructured":"Yao, S., et al.: Tree of thoughts: deliberate problem solving with large language models (2023)"},{"key":"13_CR50","doi-asserted-by":"crossref","unstructured":"Yoon, S.E., He, Z., Echterhoff, J.M., McAuley, J.: Evaluating large language models as generative user simulators for conversational recommendation (2024). arXiv preprint arXiv:2403.09738","DOI":"10.18653\/v1\/2024.naacl-long.83"},{"key":"13_CR51","doi-asserted-by":"publisher","unstructured":"Zhang, J., et al.: AgentCF: Collaborative learning with autonomous language agents for recommender systems. In: Proceedings of the ACM Web Conference 2024, pp. 3679\u20133689. WWW \u201924, Association for Computing Machinery, New York, USA (2024). https:\/\/doi.org\/10.1145\/3589334.3645537","DOI":"10.1145\/3589334.3645537"},{"key":"13_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: DIALOGPT: large-scale generative pre-training for conversational response generation (2019). arXiv preprint arXiv:1911.00536","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"13_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, X., Ai, Q., Yang, L., Croft, W.B.: Towards conversational search and recommendation: system ask, user respond. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 177\u2013186 (2018)","DOI":"10.1145\/3269206.3271776"},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhao, W.X., Bian, S., Zhou, Y., Wen, J.R., Yu, J.: Improving conversational recommender systems via knowledge graph based semantic fusion. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1006\u20131014 (2020)","DOI":"10.1145\/3394486.3403143"},{"key":"13_CR55","doi-asserted-by":"crossref","unstructured":"Zhou, K., et al.: CRSLab: an open-source toolkit for building conversational recommender system (2021). arXiv preprint arXiv:2101.00939","DOI":"10.18653\/v1\/2021.acl-demo.22"},{"key":"13_CR56","unstructured":"Zhou, P., et al.: Self-discover: large language models self-compose reasoning structures (2024). https:\/\/arxiv.org\/abs\/2402.03620"},{"key":"13_CR57","doi-asserted-by":"publisher","unstructured":"Zou, J., Kanoulas, E., Ren, P., Ren, Z., Sun, A., Long, C.: Improving conversational recommender systems via transformer-based sequential modelling. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2319\u20132324. ACM (2022). https:\/\/doi.org\/10.1145\/3477495.3531852","DOI":"10.1145\/3477495.3531852"},{"key":"13_CR58","doi-asserted-by":"publisher","unstructured":"Zou, J., Kanoulas, E., Ren, P., Ren, Z., Sun, A., Long, C.: Improving conversational recommender systems via transformer-based sequential modelling. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2319\u20132324. SIGIR \u201922, Association for Computing Machinery, New York, USA (2022). https:\/\/doi.org\/10.1145\/3477495.3531852","DOI":"10.1145\/3477495.3531852"}],"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_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:01:58Z","timestamp":1774357318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21300-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032212993","9783032213006"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21300-6_13","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"}}]}}