{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T11:51:23Z","timestamp":1768305083289,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819530007","type":"print"},{"value":"9789819530014","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"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\/978-981-95-3001-4_31","type":"book-chapter","created":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T19:42:16Z","timestamp":1763322136000},"page":"415-429","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Models Are Not Stable Recommender Systems: A Position Bias Perspective"],"prefix":"10.1007","author":[{"given":"Tianhui","family":"Ma","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Zhi","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Bowman, S.R.: Eight things to know about large language models. arXiv preprint arXiv:2304.00612 (2023)","DOI":"10.1215\/2834703X-11556011"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Dai, S., et al.: Uncovering chatgpt\u2019s capabilities in recommender systems (2023)","DOI":"10.1145\/3604915.3610646"},{"key":"31_CR3","unstructured":"Dong, Q., et al.: A survey for in-context learning. arXiv preprint arXiv:2301.00234 (2022)"},{"issue":"2","key":"31_CR4","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/s00355-011-0603-9","volume":"40","author":"P Emerson","year":"2013","unstructured":"Emerson, P.: The original borda count and partial voting. Soc. Choice Welf. 40(2), 353\u2013358 (2013)","journal-title":"Soc. Choice Welf."},{"key":"31_CR5","unstructured":"Gao, Y., et al.: Chat-rec: Towards interactive and explainable llms-augmented recommender system (2023)"},{"issue":"8","key":"31_CR6","doi-asserted-by":"publisher","first-page":"3549","DOI":"10.1109\/TKDE.2020.3028705","volume":"34","author":"Q Guo","year":"2022","unstructured":"Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., He, Q.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. 34(8), 3549\u20133568 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.J.: Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW, pp. 507\u2013517. ACM (2016)","DOI":"10.1145\/2872427.2883037"},{"key":"31_CR8","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: ICLR (Poster) (2016)"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Hou, Y., He, Z., McAuley, J., Zhao, W.X.: Learning vector-quantized item representation for transferable sequential recommenders. In: Proceedings of the ACM Web Conference 2023, pp. 1162\u20131171 (2023)","DOI":"10.1145\/3543507.3583434"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.R.: Towards universal sequence representation learning for recommender systems. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 585\u2013593 (2022)","DOI":"10.1145\/3534678.3539381"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., Zhao, W.X.: Large language models are zero-shot rankers for recommender systems (2023)","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"31_CR12","doi-asserted-by":"publisher","unstructured":"Kang, W., et al.: Do llms understand user preferences? evaluating llms on user rating prediction. CoRR abs\/2305.06474 (2023). https:\/\/doi.org\/10.48550\/ARXIV.2305.06474","DOI":"10.48550\/ARXIV.2305.06474"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In: ACL (1), pp. 8086\u20138098. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.acl-long.556"},{"issue":"4","key":"31_CR14","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s41019-023-00228-5","volume":"8","author":"X Meng","year":"2023","unstructured":"Meng, X., Huo, H., Zhang, X., Wang, W., Zhu, J.: A survey of personalized news recommendation. Data Sci. Eng. 8(4), 396\u2013416 (2023)","journal-title":"Data Sci. Eng."},{"key":"31_CR15","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"31_CR16","doi-asserted-by":"publisher","unstructured":"Petrov, A.V., Macdonald, C.: Generative sequential recommendation with gptrec. CoRR abs\/2306.11114 (2023). https:\/\/doi.org\/10.48550\/ARXIV.2306.11114","DOI":"10.48550\/ARXIV.2306.11114"},{"issue":"3","key":"31_CR17","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21\u201345 (2006)","journal-title":"IEEE Circuits Syst. Mag."},{"key":"31_CR18","doi-asserted-by":"publisher","unstructured":"Qiu, Z., Wu, X., Gao, J., Fan, W.: U-BERT: pre-training user representations for improved recommendation. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pp. 4320\u20134327. AAAI Press (2021). https:\/\/doi.org\/10.1609\/AAAI.V35I5.16557","DOI":"10.1609\/AAAI.V35I5.16557"},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Robertson, S., Zaragoza, H., et\u00a0al.: The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retrieval 3(4), 333\u2013389 (2009)","DOI":"10.1561\/1500000019"},{"key":"31_CR20","unstructured":"Wang, L., Lim, E.P.: Zero-shot next-item recommendation using large pretrained language models. arXiv preprint arXiv:2304.03153 (2023)"},{"key":"31_CR21","unstructured":"Wang, P., et al.: Large language models are not fair evaluators (2023)"},{"key":"31_CR22","unstructured":"Wang, X., et al.: Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net (2023). https:\/\/openreview.net\/pdf?id=1PL1NIMMrw"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Qi, T., Liu, Q., Tian, X., Li, J., He, W., Huang, Y., Xie, X.: Feedrec: news feed recommendation with various user feedbacks. In: Proceedings of the ACM Web Conference 2022, pp. 2088\u20132097 (2022)","DOI":"10.1145\/3485447.3512082"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Wu, F., et al.: MIND: a large-scale dataset for news recommendation. In: ACL, pp. 3597\u20133606. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"31_CR25","unstructured":"Wu, L., et al.: A survey on large language models for recommendation (2023)"},{"key":"31_CR26","unstructured":"Zhang, Y., Ding, H., Shui, Z., Ma, Y., Zou, J., Deoras, A., Wang, H.: Language models as recommender systems: Evaluations and limitations. In: I (Still) Can\u2019t Believe It\u2019s Not Better! NeurIPS 2021 Workshop (2021). https:\/\/openreview.net\/forum?id=hFx3fY7-m9b"},{"key":"31_CR27","unstructured":"Zhao, Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate before use: Improving few-shot performance of language models. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 12697\u201312706. PMLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3001-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:37:26Z","timestamp":1768282646000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3001-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"ISBN":["9789819530007","9789819530014"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3001-4_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,17]]},"assertion":[{"value":"17 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"4 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2025.scimeeting.cn\/en\/web\/index\/27434","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}