{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:57:19Z","timestamp":1780473439477,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":62,"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":"The National Key Research and Development Program of China","award":["2022YFB3104701"],"award-info":[{"award-number":["2022YFB3104701"]}]},{"name":"The CCCD Key Lab of Ministry of Culture and Tourism"},{"name":"The National Natural Science Foundation of China","award":["U21B2026 and 62272437"],"award-info":[{"award-number":["U21B2026 and 62272437"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3648158","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"4717-4726","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Item-side Fairness of Large Language Model-based Recommendation System"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9163-8979","authenticated-orcid":false,"given":"Meng","family":"Jiang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5910-0204","authenticated-orcid":false,"given":"Keqin","family":"Bao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0251-465X","authenticated-orcid":false,"given":"Jizhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5199-1428","authenticated-orcid":false,"given":"Wenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8094-0978","authenticated-orcid":false,"given":"Zhengyi","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-9842","authenticated-orcid":false,"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8472-7992","authenticated-orcid":false,"given":"Xiangnan","family":"He","sequence":"additional","affiliation":[{"name":"MoE Key Lab of BIPC & University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"2023 a. A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems. CoRR","author":"Bao Keqin","year":"2023","unstructured":"Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Fuli Feng, Xiangnan He, and Qi Tian. 2023 a. A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems. CoRR , Vol. abs\/2308.08434 (2023)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023 b. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys. ACM 1007--1014.","DOI":"10.1145\/3604915.3608857"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Keqin Bao Jizhi Zhang Yang Zhang Wang Wenjie Fuli Feng and Xiangnan He. 2023 c. Large Language Models for Recommendation: Progresses and Future Directions. In SIGIR-AP.","DOI":"10.1145\/3624918.3629550"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Asia J Biega Krishna P Gummadi and Gerhard Weikum. 2018. Equity of attention: Amortizing individual fairness in rankings. In SIGIR. 405--414.","DOI":"10.1145\/3209978.3210063"},{"key":"e_1_3_2_2_5_1","volume-title":"NeurIPS. Curran Associates","author":"Brown Tom","year":"1877","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris 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. In NeurIPS. Curran Associates, Inc., 1877--1901."},{"key":"e_1_3_2_2_6_1","volume-title":"Multisided Fairness for Recommendation. CoRR","author":"Burke Robin","year":"2017","unstructured":"Robin Burke. 2017. Multisided Fairness for Recommendation. CoRR , Vol. abs\/1707.00093 (2017)."},{"key":"e_1_3_2_2_7_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR , Vol. abs\/1810.04805 (2018)."},{"key":"e_1_3_2_2_8_1","volume-title":"Recommender Systems in the Era of Large Language Models (LLMs). CoRR","author":"Fan Wenqi","year":"2046","unstructured":"Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023. Recommender Systems in the Era of Large Language Models (LLMs). CoRR , Vol. abs\/2307.02046 (2023)."},{"key":"e_1_3_2_2_9_1","volume-title":"Zheng Liu, Chaozhuo Li, and Xing Xie.","author":"Fan Xinyan","year":"2022","unstructured":"Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li, and Xing Xie. 2022. Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation. In SIGIR. 1599--1610."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Yingqiang Ge Shuchang Liu Ruoyuan Gao Yikun Xian Yunqi Li Xiangyu Zhao Changhua Pei Fei Sun Junfeng Ge Wenwu Ou et al. 2021. Towards long-term fairness in recommendation. In WSDM. 445--453.","DOI":"10.1145\/3437963.3441824"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Yingqiang Ge Xiaoting Zhao Lucia Yu Saurabh Paul Diane Hu Chu-Cheng Hsieh and Yongfeng Zhang. 2022. Toward Pareto efficient fairness-utility trade-off in recommendation through reinforcement learning. In WSDM. 316--324.","DOI":"10.1145\/3488560.3498487"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Ido Guy Naama Zwerdling Inbal Ronen David Carmel and Erel Uziel. 2010. Social Media Recommendation Based on People and Tags. In SIGIR. 194--201.","DOI":"10.1145\/1835449.1835484"},{"key":"e_1_3_2_2_13_1","volume-title":"Konstan","author":"Maxwell Harper F.","year":"2016","unstructured":"F. Maxwell Harper and Joseph A. Konstan. 2016. The MovieLens Datasets: History and Context. TIIS (2016), 19:1--19:19."},{"key":"e_1_3_2_2_14_1","volume-title":"Chawla","author":"Hoens T. Ryan","year":"2010","unstructured":"T. Ryan Hoens, Marina Blanton, and Nitesh V. Chawla. 2010. Reliable medical recommendation systems with patient privacy. In IHI, Tiffany C. Veinot, \u00dc mit V. cC ataly\u00fc rek, Gang Luo, Henrique Andrade, and Neil R. Smalheiser (Eds.). ACM, 173--182."},{"key":"e_1_3_2_2_15_1","volume-title":"Large Language Models are Zero-Shot Rankers for Recommender Systems. CoRR","author":"Hou Yupeng","year":"2023","unstructured":"Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian J. McAuley, and Wayne Xin Zhao. 2023. Large Language Models are Zero-Shot Rankers for Recommender Systems. CoRR , Vol. abs\/2305.08845 (2023)."},{"key":"e_1_3_2_2_16_1","volume-title":"FAT (Proceedings of Machine Learning Research","volume":"201","author":"Kamishima Toshihiro","year":"2018","unstructured":"Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2018. Recommendation Independence. In FAT (Proceedings of Machine Learning Research, Vol. 81). PMLR, 187--201."},{"key":"e_1_3_2_2_17_1","volume-title":"McAuley","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In ICDM. IEEE Computer Society, 197--206."},{"key":"e_1_3_2_2_18_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_2_19_1","volume-title":"Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa.","author":"Kojima Takeshi","year":"2022","unstructured":"Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large Language Models are Zero-Shot Reasoners. In NeurIPS."},{"key":"e_1_3_2_2_20_1","volume-title":"What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media","author":"Lakkaraju Himabindu","unstructured":"Himabindu Lakkaraju, Julian J. McAuley, and Jure Leskovec. 2013. What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media. In ICWSM. The AAAI Press."},{"key":"e_1_3_2_2_21_1","volume-title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","author":"Lewis Mike","year":"2020","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In ACL. Association for Computational Linguistics, 7871--7880."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Jie Li Yongli Ren and Ke Deng. 2022. FairGAN: GANs-based fairness-aware learning for recommendations with implicit feedback. In WWW. 297--307.","DOI":"10.1145\/3485447.3511958"},{"key":"e_1_3_2_2_23_1","volume-title":"2023 b. Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights. arXiv preprint arXiv:2305.11700","author":"Li Ruyu","year":"2023","unstructured":"Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, Jiaqi Zhang, and Fajie Yuan. 2023 b. Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights. arXiv preprint arXiv:2305.11700 (2023)."},{"key":"e_1_3_2_2_24_1","volume-title":"2023 a. Fairness in Recommendation: Foundations, Methods, and Applications. TIST","author":"Li Yunqi","year":"2023","unstructured":"Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2023 a. Fairness in Recommendation: Foundations, Methods, and Applications. TIST (2023), 1--48."},{"key":"e_1_3_2_2_25_1","volume-title":"2023 a. How Can Recommender Systems Benefit from Large Language Models: A Survey. CoRR","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, and Weinan Zhang. 2023 a. How Can Recommender Systems Benefit from Large Language Models: A Survey. CoRR , Vol. abs\/2306.05817 (2023)."},{"key":"e_1_3_2_2_26_1","volume-title":"2023 b. ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation. CoRR","author":"Lin Jianghao","year":"2023","unstructured":"Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2023 b. ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation. CoRR , Vol. abs\/2308.11131 (2023)."},{"key":"e_1_3_2_2_27_1","unstructured":"Kun Lin Nasim Sonboli Bamshad Mobasher and Robin Burke. 2019. Crank up the Volume: Preference Bias Amplification in Collaborative Recommendation. In RecSys. CEUR-WS.org."},{"key":"e_1_3_2_2_28_1","volume-title":"2023 c. A Multi-facet Paradigm to Bridge Large Language Model and Recommendation. CoRR","author":"Lin Xinyu","year":"2023","unstructured":"Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2023 c. A Multi-facet Paradigm to Bridge Large Language Model and Recommendation. CoRR , Vol. abs\/2310.06491 (2023)."},{"key":"e_1_3_2_2_29_1","volume-title":"Is ChatGPT a Good Recommender? A Preliminary Study. CoRR","author":"Liu Junling","year":"2023","unstructured":"Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023. Is ChatGPT a Good Recommender? A Preliminary Study. CoRR , Vol. abs\/2304.10149 (2023)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2958761"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Marco Morik Ashudeep Singh Jessica Hong and Thorsten Joachims. 2020. Controlling fairness and bias in dynamic learning-to-rank. In SIGIR. 429--438.","DOI":"10.1145\/3397271.3401100"},{"key":"e_1_3_2_2_32_1","volume-title":"A Combined Relevance Feedback Approach for User Recommendation in E-commerce Applications","author":"Moscato Vincenzo","unstructured":"Vincenzo Moscato, Antonio Picariello, and Antonio Maria Rinaldi. 2010. A Combined Relevance Feedback Approach for User Recommendation in E-commerce Applications. In ACHI, Ray Jarvis and Cosmin Dini (Eds.). IEEE Computer Society, 209--214."},{"key":"e_1_3_2_2_33_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. CoRR Vol. abs\/2303.08774 (2023)."},{"key":"e_1_3_2_2_34_1","first-page":"27730","article-title":"2022. Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. NeurIPS , Vol. 35 (2022), 27730--27744.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_35_1","volume-title":"Berkant Barla Cambazoglu, and Aristides Gionis","author":"Paparrizos Ioannis K.","year":"2011","unstructured":"Ioannis K. Paparrizos, Berkant Barla Cambazoglu, and Aristides Gionis. 2011. Machine learned job recommendation. In RecSys, Bamshad Mobasher, Robin D. Burke, Dietmar Jannach, and Gediminas Adomavicius (Eds.). ACM, 325--328."},{"key":"e_1_3_2_2_36_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_3_2_2_37_1","volume-title":"Lipton","author":"Roberts Nicholas","year":"2020","unstructured":"Nicholas Roberts, Davis Liang, Graham Neubig, and Zachary C. Lipton. 2020. Decoding and Diversity in Machine Translation. CoRR , Vol. abs\/2011.13477 (2020)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Fatemeh Sarvi Maria Heuss Mohammad Aliannejadi Sebastian Schelter and Maarten de Rijke. 2022. Understanding and mitigating the effect of outliers in fair ranking. In WSDM. 861--869.","DOI":"10.1145\/3488560.3498441"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Danielle Saunders Rosie Sallis and Bill Byrne. 2022. First the Worst: Finding Better Gender Translations During Beam Search. In Findings of ACL. 3814--3823.","DOI":"10.18653\/v1\/2022.findings-acl.301"},{"key":"e_1_3_2_2_40_1","unstructured":"Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In ICML. PMLR 1670--1679."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Qijie Shen Wanjie Tao Jing Zhang Hong Wen Zulong Chen and Quan Lu. 2021. Sar-net: a scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios. In CIKM. 4094--4103.","DOI":"10.1145\/3459637.3481948"},{"key":"e_1_3_2_2_42_1","volume-title":"Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems. CoRR","author":"Shi Tianhao","year":"2023","unstructured":"Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, and Qi Tian. 2023. Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems. CoRR , Vol. abs\/2312.15599 (2023)."},{"key":"e_1_3_2_2_43_1","volume-title":"RecSys","author":"Steck Harald","unstructured":"Harald Steck. 2018. Calibrated Recommendations. In RecSys. Association for Computing Machinery, 154--162."},{"key":"e_1_3_2_2_44_1","volume-title":"Baptiste Rozi\u00e8 re, Naman Goyal, Eric Hambro, Faisal Azhar, Aur\u00e9 lien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample.","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9 e Lacroix, Baptiste Rozi\u00e8 re, Naman Goyal, Eric Hambro, Faisal Azhar, Aur\u00e9 lien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. CoRR , Vol. abs\/2302.13971 (2023)."},{"key":"e_1_3_2_2_45_1","volume-title":"Pingree","author":"Turcotte Jason","year":"2015","unstructured":"Jason Turcotte, Chance York, Jacob Irving, Rosanne M. Scholl, and Raymond J. Pingree. 2015. News Recommendations from Social Media Opinion Leaders: Effects on Media Trust and Information Seeking. J COMPUT-MEDIAT COMM (2015), 520--535."},{"key":"e_1_3_2_2_46_1","volume-title":"2023 a. Generative Recommendation: Towards Next-generation Recommender Paradigm. CoRR","author":"Wang Wenjie","year":"2023","unstructured":"Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, and Tat-Seng Chua. 2023 a. Generative Recommendation: Towards Next-generation Recommender Paradigm. CoRR , Vol. abs\/2304.03516 (2023)."},{"key":"e_1_3_2_2_47_1","volume-title":"Jilin Chen, and Alex Beutel.","author":"Wang Xuezhi","year":"2021","unstructured":"Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H Chi, Jilin Chen, and Alex Beutel. 2021. Practical compositional fairness: Understanding fairness in multi-component recommender systems. In WSDM. 436--444."},{"key":"e_1_3_2_2_48_1","volume-title":"2023 b. A survey on the fairness of recommender systems. TOIS","author":"Wang Yifan","year":"2023","unstructured":"Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023 b. A survey on the fairness of recommender systems. TOIS (2023), 1--43."},{"key":"e_1_3_2_2_49_1","volume-title":"TMLR","volume":"2022","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, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022a. Emergent Abilities of Large Language Models. TMLR , Vol. 2022 (2022)."},{"key":"e_1_3_2_2_50_1","volume-title":"Quoc V. Le, and Denny Zhou.","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022b. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In NeurIPS."},{"key":"e_1_3_2_2_51_1","volume-title":"A Survey on Large Language Models for Recommendation. CoRR","author":"Wu Likang","year":"1986","unstructured":"Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2023. A Survey on Large Language Models for Recommendation. CoRR , Vol. abs\/2305.19860 (2023)."},{"key":"e_1_3_2_2_52_1","volume-title":"Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. CoRR","author":"Xi Yunjia","year":"2023","unstructured":"Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu. 2023. Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. CoRR , Vol. abs\/2306.10933 (2023)."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Zhengyi Yang Xiangnan He Jizhi Zhang Jiancan Wu Xin Xin Jiawei Chen and Xiang Wang. 2023 a. A Generic Learning Framework for Sequential Recommendation with Distribution Shifts. In SIGIR. ACM 331--340.","DOI":"10.1145\/3539618.3591624"},{"key":"e_1_3_2_2_54_1","unstructured":"Zhengyi Yang Jiancan Wu Yanchen Luo Jizhi Zhang Yancheng Yuan An Zhang Xiang Wang and Xiangnan He. 2023 b. Large Language Model Can Interpret Latent Space of Sequential Recommender. arxiv: 2310.20487 [cs.IR]"},{"key":"e_1_3_2_2_55_1","volume-title":"Beyond Parity: Fairness Objectives for Collaborative Filtering. In NeurIPS. 2921--2930.","author":"Yao Sirui","year":"2017","unstructured":"Sirui Yao and Bert Huang. 2017. Beyond Parity: Fairness Objectives for Collaborative Filtering. In NeurIPS. 2921--2930."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"crossref","unstructured":"Jizhi Zhang Keqin Bao Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023 a. Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. In RecSys. ACM 993--999.","DOI":"10.1145\/3604915.3608860"},{"key":"e_1_3_2_2_57_1","volume-title":"Leyu Lin, and Ji-Rong Wen. 2023 c. Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach. CoRR","author":"Zhang Junjie","year":"2023","unstructured":"Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023 c. Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach. CoRR (2023)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"crossref","unstructured":"Yang Zhang Fuli Feng Xiangnan He Tianxin Wei Chonggang Song Guohui Ling and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In SIGIR. 11--20.","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_2_2_59_1","volume-title":"2023 b. CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation. CoRR","author":"Zhang Yang","year":"1948","unstructured":"Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. 2023 b. CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation. CoRR , Vol. abs\/2310.19488 (2023)."},{"key":"e_1_3_2_2_60_1","unstructured":"Wayne Xin Zhao Kun Zhou Junyi Li et al. 2023. A Survey of Large Language Models. CoRR Vol. abs\/2303.18223 (2023)."},{"key":"e_1_3_2_2_61_1","volume-title":"The impact of YouTube recommendation system on video views","author":"Zhou Renjie","unstructured":"Renjie Zhou, Samamon Khemmarat, and Lixin Gao. 2010. The impact of YouTube recommendation system on video views. In SIGCOMM, Mark Allman (Ed.). ACM, 404--410."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhu Jingu Kim Trung Nguyen Aish Fenton and James Caverlee. 2021. Fairness among new items in cold start recommender systems. In SIGIR. 767--776.","DOI":"10.1145\/3404835.3462948"}],"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\/3589334.3648158","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3648158","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:29:51Z","timestamp":1755822591000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3648158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":62,"alternative-id":["10.1145\/3589334.3648158","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3648158","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"}}]}}