{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:11:12Z","timestamp":1757617872814,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,22]]},"DOI":"10.1145\/3705328.3748159","type":"proceedings-article","created":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T10:48:44Z","timestamp":1757155724000},"page":"832-841","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8099-7762","authenticated-orcid":false,"given":"Genki","family":"Kusano","sequence":"first","affiliation":[{"name":"NEC Corporation, Kawasaki, Kanagawa, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5608-0719","authenticated-orcid":false,"given":"Kosuke","family":"Akimoto","sequence":"additional","affiliation":[{"name":"NEC Corporation, Kawasaki, Kanagawa, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9653-8523","authenticated-orcid":false,"given":"Kunihiro","family":"Takeoka","sequence":"additional","affiliation":[{"name":"NEC Corporation, Kawasaki, Kanagawa, Japan"}]}],"member":"320","published-online":{"date-parts":[[2025,9,7]]},"reference":[{"key":"e_1_3_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3610647"},{"key":"e_1_3_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608857"},{"key":"e_1_3_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"crossref","unstructured":"Filippo Betello Antonio Purificato Federico Siciliano Giovanni Trappolini Andrea Bacciu Nicola Tonellotto and Fabrizio Silvestri. 2025. A Reproducible Analysis of Sequential Recommender Systems. IEEE Access 13 (2025) 5762\u20135772.","DOI":"10.1109\/ACCESS.2024.3522049"},{"key":"e_1_3_3_3_6_2","unstructured":"Jiangjie Chen Xintao Wang Rui Xu Siyu Yuan Yikai Zhang Wei Shi Jian Xie Shuang Li Ruihan Yang Tinghui Zhu Aili Chen Nianqi Li Lida Chen Caiyu Hu Siye Wu Scott Ren Ziquan Fu and Yanghua Xiao. 2024. From Persona to Personalization: A Survey on Role-Playing Language Agents. TMLR (2024) 2835\u20138856."},{"key":"e_1_3_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-35320-8_1"},{"key":"e_1_3_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_3_3_9_2","first-page":"101","volume-title":"RecSys","author":"Dacrema Maurizio\u00a0Ferrari","year":"2019","unstructured":"Maurizio\u00a0Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In RecSys. ACM, 101\u2013109."},{"key":"e_1_3_3_3_10_2","first-page":"1126","volume-title":"RecSys","author":"Dai Sunhao","year":"2023","unstructured":"Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023. Uncovering ChatGPT\u2019s Capabilities in Recommender Systems. In RecSys. ACM, 1126\u20131132."},{"key":"e_1_3_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3475943"},{"key":"e_1_3_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671474"},{"key":"e_1_3_3_3_13_2","unstructured":"Yihe Deng Weitong Zhang Zixiang Chen and Quanquan Gu. 2023. Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves. CoRR abs\/2311.04205 (2023)."},{"key":"e_1_3_3_3_14_2","unstructured":"Wenqi Fan Zihuai Zhao Jiatong Li Yunqing Liu Xiaowei Mei Yiqi Wang Jiliang Tang and Qing Li. 5555. Recommender Systems in the Era of Large Language Models (LLMs). TKDE 01 (5555) 1\u201320."},{"key":"e_1_3_3_3_15_2","unstructured":"Yi Fang Wenjie Wang Yang Zhang Fengbin Zhu Qifan Wang Fuli Feng and Xiangnan He. 2025. Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment. CoRR abs\/2502.02061 (2025)."},{"key":"e_1_3_3_3_16_2","unstructured":"Yunfan Gao Tao Sheng Youlin Xiang Yun Xiong Haofen Wang and Jiawei Zhang. 2023. Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System. CoRR abs\/2303.14524 (2023)."},{"key":"e_1_3_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788627"},{"key":"e_1_3_3_3_18_2","volume-title":"ICLR","author":"Halawi Danny","year":"2024","unstructured":"Danny Halawi, Jean-Stanislas Denain, and Jacob Steinhardt. 2024. Overthinking the Truth: Understanding how Language Models Process False Demonstrations. In ICLR. OpenReview.net."},{"key":"e_1_3_3_3_19_2","first-page":"173","volume-title":"WWW","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW. ACM, 173\u2013182."},{"key":"e_1_3_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614949"},{"key":"e_1_3_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"e_1_3_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-56063-7_42"},{"key":"e_1_3_3_3_23_2","doi-asserted-by":"crossref","unstructured":"Muhammad\u00a0Murad Khan Roliana Ibrahim and Imran Ghani. 2017. Cross Domain Recommender Systems: A Systematic Literature Review. ACM Comput. Surv. 50 3 (2017) 36:1\u201336:34.","DOI":"10.1145\/3073565"},{"key":"e_1_3_3_3_24_2","volume-title":"NeurIPS","author":"Kojima Takeshi","year":"2022","unstructured":"Takeshi Kojima, Shixiang\u00a0Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large Language Models are Zero-Shot Reasoners. In NeurIPS."},{"key":"e_1_3_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.228"},{"key":"e_1_3_3_3_26_2","doi-asserted-by":"crossref","unstructured":"Yehuda Koren Robert\u00a0M. Bell and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42 8 (2009) 30\u201337.","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688159"},{"key":"e_1_3_3_3_28_2","unstructured":"Cheng Li Jindong Wang Yixuan Zhang Kaijie Zhu Wenxin Hou Jianxun Lian Fang Luo Qiang Yang and Xing Xie. 2023. Large Language Models Understand and Can be Enhanced by Emotional Stimuli. CoRR abs\/2307.11760 (2023)."},{"key":"e_1_3_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615017"},{"key":"e_1_3_3_3_30_2","first-page":"10146","volume-title":"LREC\/COLING","author":"Li Lei","year":"2024","unstructured":"Lei Li, Yongfeng Zhang, Dugang Liu, and Li Chen. 2024. Large Language Models for Generative Recommendation: A Survey and Visionary Discussions. In LREC\/COLING. ELRA and ICCL, 10146\u201310159."},{"key":"e_1_3_3_3_31_2","unstructured":"Jianghao Lin Xinyi Dai Yunjia Xi Weiwen Liu Bo Chen Xiangyang Li Chenxu Zhu Huifeng Guo Yong Yu Ruiming Tang and Weinan Zhang. 2024. How Can Recommender Systems Benefit from Large Language Models: A Survey. ACM Trans. Inf. Syst. (2024). Just Accepted."},{"key":"e_1_3_3_3_32_2","unstructured":"Junling Liu Chao Liu Renjie Lv Kang Zhou and Yan Zhang. 2023. Is ChatGPT a Good Recommender? A Preliminary Study. CoRR abs\/2304.10149 (2023)."},{"key":"e_1_3_3_3_33_2","unstructured":"Jiahao Liu Xueshuo Yan Dongsheng Li Guangping Zhang Hansu Gu Peng Zhang Tun Lu Li Shang and Ning Gu. 2025. Improving LLM-powered Recommendations with Personalized Information. CoRR abs\/2502.13845 (2025)."},{"key":"e_1_3_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-5569-1_18"},{"key":"e_1_3_3_3_35_2","volume-title":"NeurIPS","author":"Madaan Aman","year":"2023","unstructured":"Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa\u00a0Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-Refine: Iterative Refinement with Self-Feedback. In NeurIPS."},{"key":"e_1_3_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1613"},{"key":"e_1_3_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/343"},{"key":"e_1_3_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-short.35"},{"key":"e_1_3_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688073"},{"key":"e_1_3_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1018"},{"key":"e_1_3_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3548487"},{"key":"e_1_3_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412488"},{"key":"e_1_3_3_3_43_2","unstructured":"Pranab Sahoo Ayush\u00a0Kumar Singh Sriparna Saha Vinija Jain Samrat Mondal and Aman Chadha. 2024. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. CoRR abs\/2402.07927 (2024)."},{"key":"e_1_3_3_3_44_2","unstructured":"Yang Sui Yu-Neng Chuang Guanchu Wang Jiamu Zhang Tianyi Zhang Jiayi Yuan Hongyi Liu Andrew Wen Shaochen Zhong Hanjie Chen and Xia\u00a0Ben Hu. 2025. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models. CoRR abs\/2503.16419 (2025)."},{"key":"e_1_3_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412489"},{"key":"e_1_3_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.969"},{"key":"e_1_3_3_3_48_2","unstructured":"Shubham Vatsal and Harsh Dubey. 2024. A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks. CoRR abs\/2407.12994 (2024)."},{"key":"e_1_3_3_3_49_2","unstructured":"Lei Wang and Ee-Peng Lim. 2023. Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. CoRR abs\/2304.03153 (2023)."},{"key":"e_1_3_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-naacl.56"},{"key":"e_1_3_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.147"},{"key":"e_1_3_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.878"},{"key":"e_1_3_3_3_53_2","volume-title":"ICLR","author":"Wang Xuezhi","year":"2023","unstructured":"Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc\u00a0V. Le, Ed\u00a0H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-Consistency Improves Chain of Thought Reasoning in Language Models. In ICLR. OpenReview.net."},{"key":"e_1_3_3_3_54_2","volume-title":"NeurIPS","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed\u00a0H. Chi, Quoc\u00a0V. Le, and Denny Zhou. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In NeurIPS."},{"key":"e_1_3_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475665"},{"key":"e_1_3_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"e_1_3_3_3_57_2","doi-asserted-by":"crossref","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. 2024. A survey on large language models for recommendation. World Wide Web (WWW) 27 5 (2024) 60.","DOI":"10.1007\/s11280-024-01291-2"},{"key":"e_1_3_3_3_58_2","unstructured":"Lanling Xu Junjie Zhang Bingqian Li Jinpeng Wang Sheng Chen Wayne\u00a0Xin Zhao and Ji-Rong Wen. 2025. Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis. ACM Transactions on Knowledge Discovery from Data (2025). Just Accepted."},{"key":"e_1_3_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.871"},{"key":"e_1_3_3_3_60_2","volume-title":"ICLR","author":"Yang Chengrun","year":"2024","unstructured":"Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc\u00a0V. Le, Denny Zhou, and Xinyun Chen. 2024. Large Language Models as Optimizers. In ICLR. OpenReview.net."},{"key":"e_1_3_3_3_61_2","volume-title":"ICLR","author":"Yao Shunyu","year":"2023","unstructured":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik\u00a0R. Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. In ICLR. OpenReview.net."},{"key":"e_1_3_3_3_62_2","unstructured":"Zhenrui Yue Sara Rabhi Gabriel de Souza Pereira\u00a0Moreira Dong Wang and Even Oldridge. 2023. LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking. CoRR abs\/2311.02089 (2023)."},{"key":"e_1_3_3_3_63_2","doi-asserted-by":"crossref","unstructured":"Tianzi Zang Yanmin Zhu Haobing Liu Ruohan Zhang and Jiadi Yu. 2023. A Survey on Cross-domain Recommendation: Taxonomies Methods and Future Directions. ACM Trans. Inf. Syst. 41 2 (2023) 42:1\u201342:39.","DOI":"10.1145\/3548455"},{"key":"e_1_3_3_3_64_2","volume-title":"ICLR","author":"Zheng Huaixiu\u00a0Steven","year":"2024","unstructured":"Huaixiu\u00a0Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed\u00a0H. Chi, Quoc\u00a0V. Le, and Denny Zhou. 2024. Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models. In ICLR. OpenReview.net."},{"key":"e_1_3_3_3_65_2","first-page":"3207","volume-title":"WWW","author":"Zheng Zhi","year":"2024","unstructured":"Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. 2024. Harnessing Large Language Models for Text-Rich Sequential Recommendation. In WWW. ACM, 3207\u20133216."},{"key":"e_1_3_3_3_66_2","unstructured":"Joyce Zhou Yijia Dai and Thorsten Joachims. 2024. Language-Based User Profiles for Recommendation. CoRR abs\/2402.15623 (2024)."},{"key":"e_1_3_3_3_67_2","first-page":"928","volume-title":"WWW","author":"Zhou Zhihui","year":"2023","unstructured":"Zhihui Zhou, Lilin Zhang, and Ning Yang. 2023. Contrastive Collaborative Filtering for Cold-Start Item Recommendation. In WWW. ACM, 928\u2013937."},{"key":"e_1_3_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401178"}],"event":{"name":"RecSys '25: Nineteenth ACM Conference on Recommender Systems","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGIR ACM Special Interest Group on Information Retrieval","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Prague Czech Republic","acronym":"RecSys '25"},"container-title":["Proceedings of the Nineteenth ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705328.3748159","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:48:33Z","timestamp":1757159313000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705328.3748159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,7]]},"references-count":67,"alternative-id":["10.1145\/3705328.3748159","10.1145\/3705328"],"URL":"https:\/\/doi.org\/10.1145\/3705328.3748159","relation":{},"subject":[],"published":{"date-parts":[[2025,9,7]]},"assertion":[{"value":"2025-09-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}