{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:31:43Z","timestamp":1790357503441,"version":"4.1.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"\"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang","award":["2025C02037"],"award-info":[{"award-number":["2025C02037"]}]},{"name":"National Natural Science Foundation of China","award":["62376243"],"award-info":[{"award-number":["62376243"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3817477","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:47Z","timestamp":1786036547000},"page":"8858-8869","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0284-671X","authenticated-orcid":false,"given":"Kairui","family":"Fu","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8802-1006","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2876-6601","authenticated-orcid":false,"given":"Shuwen","family":"Xiao","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9310-7813","authenticated-orcid":false,"given":"Ziyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3432-3726","authenticated-orcid":false,"given":"Xinming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9034-4506","authenticated-orcid":false,"given":"Chenchi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1456-6224","authenticated-orcid":false,"given":"Yuliang","family":"Yan","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5056-6776","authenticated-orcid":false,"given":"Junjun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1691-4175","authenticated-orcid":false,"given":"Xiangheng","family":"Kong","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-8289","authenticated-orcid":false,"given":"Shengyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7528-8131","authenticated-orcid":false,"given":"Kun","family":"Kuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1665-3025","authenticated-orcid":false,"given":"Yuning","family":"Jiang","sequence":"additional","affiliation":[{"name":"Taobao & Tmall Group of Alibaba, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Alibaba. 2025. RTP-LLM: A High-Performance LLM Inference Engine. https:\/\/github.com\/alibaba\/rtp-llm."},{"key":"e_1_3_2_1_2_1","volume-title":"Yelp dataset challenge: Review rating prediction. arXiv preprint arXiv:1605.05362","author":"Asghar Nabiha","year":"2016","unstructured":"Nabiha Asghar. 2016. Yelp dataset challenge: Review rating prediction. arXiv preprint arXiv:1605.05362 (2016)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"e_1_3_2_1_4_1","volume-title":"V46","author":"Bobadilla Jes\u00fas","year":"2013","unstructured":"Jes\u00fas Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Guti\u00e9rrez. 2013. Recommender systems survey. Knowledge-based systems, V46 (2013), 109-132."},{"key":"e_1_3_2_1_5_1","unstructured":"Ben Chen Xian Guo Siyuan Wang Zihan Liang Yue Lv Yufei Ma Xinlong Xiao Bowen Xue Xuxin Zhang Ying Yang et al. 2025. OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search. arXiv preprint arXiv:2509.03236 (2025)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.522"},{"key":"e_1_3_2_1_7_1","volume-title":"Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment. arXiv preprint arXiv:2502.18965","author":"Deng Jiaxin","year":"2025","unstructured":"Jiaxin Deng, Shiyao Wang, Kuo Cai, Lejian Ren, Qigen Hu, Weifeng Ding, Qiang Luo, and Guorui Zhou. 2025. Onerec: Unifying retrieve and rank with generative recommender and iterative preference alignment. arXiv preprint arXiv:2502.18965 (2025)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"e_1_3_2_1_9_1","volume-title":"Beam search strategies for neural machine translation. arXiv preprint arXiv:1702.01806","author":"Freitag Markus","year":"2017","unstructured":"Markus Freitag and Yaser Al-Onaizan. 2017. Beam search strategies for neural machine translation. arXiv preprint arXiv:1702.01806 (2017)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709178"},{"key":"e_1_3_2_1_11_1","volume-title":"End-to-End Optimization of Quantization-Based Structure Learning and Interventional Next-Item Recommendation. In CAAI International Conference on Artificial Intelligence. Springer, 415-429","author":"Fu Kairui","year":"2023","unstructured":"Kairui Fu, Qiaowei Miao, Shengyu Zhang, Kun Kuang, and Fei Wu. 2023. End-to-End Optimization of Quantization-Based Structure Learning and Interventional Next-Item Recommendation. In CAAI International Conference on Artificial Intelligence. Springer, 415-429."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671669"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557220"},{"key":"e_1_3_2_1_14_1","volume-title":"Optimized product quantization","author":"Ge Tiezheng","year":"2013","unstructured":"Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun. 2013. Optimized product quantization. IEEE transactions on pattern analysis and machine intelligence, V36, 4 (2013), 744-755."},{"key":"e_1_3_2_1_15_1","volume-title":"V5, 4","author":"Maxwell Harper F","year":"2015","unstructured":"F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), V5, 4 (2015), 1-19."},{"key":"e_1_3_2_1_16_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736979"},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 4237-4245","author":"Jiang Bo","year":"2024","unstructured":"Bo Jiang, Yao Lu, Guangming Lu, and Bob Zhang. 2024. QFormer: an efficient quaternion transformer for image denoising. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 4237-4245."},{"key":"e_1_3_2_1_19_1","volume-title":"Tong Zhao, and Neil Shah.","author":"Ju Clark Mingxuan","year":"2025","unstructured":"Clark Mingxuan Ju, Liam Collins, Leonardo Neves, Bhuvesh Kumar, Louis Yufeng Wang, Tong Zhao, and Neil Shah. 2025. Generative Recommendation with Semantic IDs: A Practitioner's Handbook. arXiv preprint arXiv:2507.22224 (2025)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01123"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357814"},{"key":"e_1_3_2_1_23_1","volume-title":"Bbqrec: Behavior-bind quantization for multi-modal sequential recommendation. arXiv preprint arXiv:2504.06636","author":"Li Kaiyuan","year":"2025","unstructured":"Kaiyuan Li, Rui Xiang, Yong Bai, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, and Kun Gai. 2025. Bbqrec: Behavior-bind quantization for multi-modal sequential recommendation. arXiv preprint arXiv:2504.06636 (2025)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Zida Liang Changfa Wu Dunxian Huang Weiqiang Sun Ziyang Wang Yuliang Yan Jian Wu Yuning Jiang Bo Zheng Ke Chen et al. 2025. TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios. arXiv preprint arXiv:2508.11977 (2025).","DOI":"10.1145\/3746252.3761557"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730053"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729989"},{"key":"e_1_3_2_1_27_1","volume-title":"RecFlow: An Industrial Full Flow Recommendation Dataset. In The Thirteenth International Conference on Learning Representations.","author":"Liu Qi","year":"2025","unstructured":"Qi Liu, Kai Zheng, Rui Huang, Wuchao Li, Kuo Cai, Yuan Chai, Yanan Niu, Yiqun Hui, Bing Han, Na Mou, et al., 2025a. RecFlow: An Industrial Full Flow Recommendation Dataset. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_1_28_1","volume-title":"Negative sampling in recommendation: A survey and future directions. arXiv preprint arXiv:2409.07237","author":"Ma Haokai","year":"2024","unstructured":"Haokai Ma, Ruobing Xie, Lei Meng, Fuli Feng, Xiaoyu Du, Xingwu Sun, Zhanhui Kang, and Xiangxu Meng. 2024. Negative sampling in recommendation: A survey and future directions. arXiv preprint arXiv:2409.07237 (2024)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102422"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.146"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3705328.3759300"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688123"},{"key":"e_1_3_2_1_33_1","volume-title":"International conference on machine learning. PmLR, 8748-8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PmLR, 8748-8763."},{"key":"e_1_3_2_1_34_1","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, V21, 140 (2020), 1-67.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_35_1","volume-title":"Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al.","author":"Rajput Shashank","year":"2023","unstructured":"Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al., 2023. Recommender systems with generative retrieval. Advances in Neural Information Processing Systems, V36 (2023), 10299-10315."},{"key":"e_1_3_2_1_36_1","volume-title":"V36","author":"Sun Weiwei","year":"2023","unstructured":"Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang, Haichao Zhu, Pengjie Ren, Zhumin Chen, Dawei Yin, Maarten Rijke, and Zhaochun Ren. 2023. Learning to tokenize for generative retrieval. Advances in Neural Information Processing Systems, V36 (2023), 46345-46361."},{"key":"e_1_3_2_1_37_1","volume-title":"Qwen2 technical report. arXiv preprint arXiv:2407.10671","author":"Team Qwen","year":"2024","unstructured":"Qwen Team. 2024. Qwen2 technical report. arXiv preprint arXiv:2407.10671 (2024)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679569"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679692"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729934"},{"key":"e_1_3_2_1_41_1","volume-title":"Chinese clip: Contrastive vision-language pretraining in chinese. arXiv preprint arXiv:2211.01335","author":"Yang An","year":"2022","unstructured":"An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, and Chang Zhou. 2022. Chinese clip: Contrastive vision-language pretraining in chinese. arXiv preprint arXiv:2211.01335 (2022)."},{"key":"e_1_3_2_1_42_1","volume-title":"Yun He, Xue Feng, Nima Noorshams, Sem Park, et al.","author":"Yang Liu","year":"2024","unstructured":"Liu Yang, Fabian Paischer, Kaveh Hassani, Jiacheng Li, Shuai Shao, Zhang Gabriel Li, Yun He, Xue Feng, Nima Noorshams, Sem Park, et al., 2024. Unifying generative and dense retrieval for sequential recommendation. arXiv preprint arXiv:2411.18814 (2024)."},{"key":"e_1_3_2_1_43_1","volume-title":"Large scale product graph construction for recommendation in e-commerce. arXiv preprint arXiv:2010.05525","author":"Yang Xiaoyong","year":"2020","unstructured":"Xiaoyong Yang, Yadong Zhu, Yi Zhang, Xiaobo Wang, and Quan Yuan. 2020. Large scale product graph construction for recommendation in e-commerce. arXiv preprint arXiv:2010.05525 (2020)."},{"key":"e_1_3_2_1_44_1","volume-title":"ThinkRec: Thinking-based recommendation via LLM. arXiv preprint arXiv:2505.15091","author":"Yu Qihang","year":"2025","unstructured":"Qihang Yu, Kairui Fu, Shengyu Zhang, Zheqi Lv, Fan Wu, and Fei Wu. 2025. ThinkRec: Thinking-based recommendation via LLM. arXiv preprint arXiv:2505.15091 (2025)."},{"key":"e_1_3_2_1_45_1","volume-title":"Tenrec: A large-scale multipurpose benchmark dataset for recommender systems. Advances in Neural Information Processing Systems, V35","author":"Yuan Guanghu","year":"2022","unstructured":"Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, et al., 2022. Tenrec: A large-scale multipurpose benchmark dataset for recommender systems. Advances in Neural Information Processing Systems, V35 (2022), 11480-11493."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645477"},{"key":"e_1_3_2_1_47_1","unstructured":"Jiaqi Zhai Lucy Liao Xing Liu Yueming Wang Rui Li Xuan Cao Leon Gao Zhaojie Gong Fangda Gu Michael He et al. 2024. Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations. arXiv preprint arXiv:2402.17152 (2024)."},{"key":"e_1_3_2_1_48_1","volume-title":"V52, 1","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), V52, 1 (2019), 1-38."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730314"},{"key":"e_1_3_2_1_50_1","volume-title":"Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory. arXiv preprint arXiv:2504.06714","author":"Zhao Jujia","year":"2025","unstructured":"Jujia Zhao, Wenjie Wang, Chen Xu, Xiuying Chen, Zhaochun Ren, and Suzan Verberne. 2025. Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory. arXiv preprint arXiv:2504.06714 (2025)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00118"},{"key":"e_1_3_2_1_52_1","volume-title":"Wayne Xin Zhao, and Ji-Rong Wen","author":"Zheng Bowen","year":"2025","unstructured":"Bowen Zheng, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen. 2025a. Universal Item Tokenization for Transferable Generative Recommendation. arXiv preprint arXiv:2504.04405 (2025)."},{"key":"e_1_3_2_1_53_1","volume-title":"EGA-V2: An End-to-end Generative Framework for Industrial Advertising. arXiv preprint arXiv:2505.17549","author":"Zheng Zuowu","year":"2025","unstructured":"Zuowu Zheng, Ze Wang, Fan Yang, Jiangke Fan, Teng Zhang, Yongkang Wang, and Xingxing Wang. 2025b. EGA-V2: An End-to-end Generative Framework for Industrial Advertising. arXiv preprint arXiv:2505.17549 (2025)."},{"key":"e_1_3_2_1_54_1","unstructured":"Guorui Zhou Hengrui Hu Hongtao Cheng Huanjie Wang Jiaxin Deng Jinghao Zhang Kuo Cai Lejian Ren Lu Ren Liao Yu et al. 2025. OneRec-V2 Technical Report. arXiv preprint arXiv:2508.20900 (2025)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:27:14Z","timestamp":1786066034000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3817477"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":54,"alternative-id":["10.1145\/3770855.3817477","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3817477","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}