{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:08:09Z","timestamp":1784138889032,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62502404"],"award-info":[{"award-number":["62502404"]}]},{"name":"Hong Kong Research Grants Council (Research Impact Fund)","award":["R1015-23"],"award-info":[{"award-number":["R1015-23"]}]},{"name":"Hong Kong Research Grants Council (Collaborative Research Fund)","award":["C1043-24GF"],"award-info":[{"award-number":["C1043-24GF"]}]},{"name":"Hong Kong Research Grants Council (General Research Fund)","award":["11218325"],"award-info":[{"award-number":["11218325"]}]},{"name":"Institute of Digital Medicine of City University of Hong Kong","award":["9229503"],"award-info":[{"award-number":["9229503"]}]},{"name":"CCF-Alimama Tech Kangaroo Fund","award":["2024002"],"award-info":[{"award-number":["2024002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,20]]},"DOI":"10.1145\/3805712.3809663","type":"proceedings-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:06:26Z","timestamp":1784135186000},"page":"1925-1936","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2852-9910","authenticated-orcid":false,"given":"Yejing","family":"Wang","sequence":"first","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8716-7716","authenticated-orcid":false,"given":"Shengyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5968-2656","authenticated-orcid":false,"given":"Jinyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0751-2602","authenticated-orcid":false,"given":"Qidong","family":"Liu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8294-0589","authenticated-orcid":false,"given":"Xinhang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1809-8264","authenticated-orcid":false,"given":"Wenlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0770-2107","authenticated-orcid":false,"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4285-5033","authenticated-orcid":false,"given":"Pengjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8094-1545","authenticated-orcid":false,"given":"Chuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3111-1005","authenticated-orcid":false,"given":"Jian","family":"Xu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4037-6315","authenticated-orcid":false,"given":"Bo","family":"Zheng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-4416","authenticated-orcid":false,"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"27381","article-title":"Flow network based generative models for non-iterative diverse candidate generation","volume":"34","author":"Bengio Emmanuel","year":"2021","unstructured":"Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio. 2021. Flow network based generative models for non-iterative diverse candidate generation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 27381-27394.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_2_1","first-page":"1","article-title":"Gflownet foundations","volume":"24","author":"Bengio Yoshua","year":"2023","unstructured":"Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio. 2023. Gflownet foundations. Journal of Machine Learning Research, Vol. 24, 210 (2023), 1-55.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_3_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_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3522672"},{"key":"e_1_3_2_1_5_1","volume-title":"On Softmax Direct Preference Optimization for Recommendation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems.","author":"Chen Yuxin","unstructured":"Yuxin Chen, Junfei Tan, An Zhang, Zhengyi Yang, Leheng Sheng, Enzhi Zhang, Xiang Wang, and Tat-Seng Chua. [n.d.]. On Softmax Direct Preference Optimization for Recommendation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_6_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_7_1","volume-title":"Inductive Generative Recommendation via Retrieval-based Speculation. arXiv preprint arXiv:2410.02939","author":"Ding Yijie","year":"2024","unstructured":"Yijie Ding, Yupeng Hou, Jiacheng Li, and Julian McAuley. 2024. Inductive Generative Recommendation via Retrieval-based Speculation. arXiv preprint arXiv:2410.02939 (2024)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3698878"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714524"},{"key":"e_1_3_2_1_10_1","volume-title":"Process-supervised llm recommenders via flow-guided tuning. arXiv preprint arXiv:2503.07377","author":"Gao Chongming","year":"2025","unstructured":"Chongming Gao, Mengyao Gao, Chenxiao Fan, Shuai Yuan, Wentao Shi, and Xiangnan He. 2025c. Process-supervised llm recommenders via flow-guided tuning. arXiv preprint arXiv:2503.07377 (2025)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557220"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714922"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729987"},{"key":"e_1_3_2_1_14_1","volume-title":"PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations. arXiv preprint arXiv:2601.04674","author":"Guo Chengcheng","year":"2026","unstructured":"Chengcheng Guo, Kuo Cai, Yu Zhou, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, and Guorui Zhou. 2026. PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations. arXiv preprint arXiv:2601.04674 (2026)."},{"key":"e_1_3_2_1_15_1","volume-title":"OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion. arXiv preprint arXiv:2506.06913","author":"Guo Xian","year":"2025","unstructured":"Xian Guo, Ben Chen, Siyuan Wang, Ying Yang, Chenyi Lei, Yuqing Ding, and Han Li. 2025. OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion. arXiv preprint arXiv:2506.06913 (2025)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3624918.3625339"},{"key":"e_1_3_2_1_17_1","volume-title":"Improving llms for recommendation with out-of-vocabulary tokens. arXiv preprint arXiv:2406.08477","author":"Huang Ting-Ji","year":"2024","unstructured":"Ting-Ji Huang, Jia-Qi Yang, Chunxu Shen, Kai-Qi Liu, De-Chuan Zhan, and Han-Jia Ye. 2024. Improving llms for recommendation with out-of-vocabulary tokens. arXiv preprint arXiv:2406.08477 (2024)."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning. PMLR, 9786-9801","author":"Jain Moksh","year":"2022","unstructured":"Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al., 2022. Biological sequence design with gflownets. In International Conference on Machine Learning. PMLR, 9786-9801."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1039\/D3DD00002H"},{"key":"e_1_3_2_1_20_1","volume-title":"GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks. arXiv preprint arXiv:2503.06514","author":"Kang Haoqiang","year":"2025","unstructured":"Haoqiang Kang, Enna Sachdeva, Piyush Gupta, Sangjae Bae, and Kwonjoon Lee. 2025. GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks. arXiv preprint arXiv:2503.06514 (2025)."},{"key":"e_1_3_2_1_21_1","unstructured":"Solomon Kullback. 1951. Kullback-leibler divergence."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01123"},{"key":"e_1_3_2_1_23_1","unstructured":"Xiaopeng Li Bo Chen Junda She Shiteng Cao You Wang Qinlin Jia Haiying He Zheli Zhou Zhao Liu Ji Liu et al. 2025. A survey of generative recommendation from a tri-decoupled perspective: Tokenization architecture and optimization. (2025)."},{"key":"e_1_3_2_1_24_1","volume-title":"A survey of generative search and recommendation in the era of large language models. arXiv preprint arXiv:2404.16924","author":"Li Yongqi","year":"2024","unstructured":"Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024. A survey of generative search and recommendation in the era of large language models. arXiv preprint arXiv:2404.16924 (2024)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3678004"},{"key":"e_1_3_2_1_26_1","volume-title":"Order-agnostic Identifier for Large Language Model-based Generative Recommendation. arXiv preprint arXiv:2502.10833","author":"Lin Xinyu","year":"2025","unstructured":"Xinyu Lin, Haihan Shi, Wenjie Wang, Fuli Feng, Qifan Wang, See-Kiong Ng, and Tat-Seng Chua. 2025b. Order-agnostic Identifier for Large Language Model-based Generative Recommendation. arXiv preprint arXiv:2502.10833 (2025)."},{"key":"e_1_3_2_1_27_1","volume-title":"Efficient Inference for Large Language Model-based Generative Recommendation. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ACSNlt77hq","author":"Lin Xinyu","year":"2025","unstructured":"Xinyu Lin, Chaoqun Yang, Wenjie Wang, Yongqi Li, Cunxiao Du, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2025c. Efficient Inference for Large Language Model-based Generative Recommendation. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ACSNlt77hq"},{"key":"e_1_3_2_1_28_1","volume-title":"2024 e. End-to-End Learnable Item Tokenization for Generative Recommendation. arXiv preprint arXiv:2409.05546","author":"Liu Enze","year":"2024","unstructured":"Enze Liu, Bowen Zheng, Cheng Ling, Lantao Hu, Han Li, and Wayne Xin Zhao. 2024 e. End-to-End Learnable Item Tokenization for Generative Recommendation. arXiv preprint arXiv:2409.05546 (2024)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0839"},{"key":"e_1_3_2_1_30_1","volume-title":"Large language model distilling medication recommendation model. arXiv preprint arXiv:2402.02803","author":"Liu Qidong","year":"2024","unstructured":"Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, and Yefeng Zheng. 2024d. Large language model distilling medication recommendation model. arXiv preprint arXiv:2402.02803 (2024)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736553"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729911"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599364"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679730"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671531"},{"key":"e_1_3_2_1_36_1","volume-title":"The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation. arXiv preprint arXiv:2512.10388","author":"Liu Ziwei","year":"2025","unstructured":"Ziwei Liu, Yejing Wang, Qidong Liu, Zijian Zhang, Chong Chen, Wei Huang, and Xiangyu Zhao. 2025a. The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation. arXiv preprint arXiv:2512.10388 (2025)."},{"key":"e_1_3_2_1_37_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_38_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0431"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_40_1","volume-title":"Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian.","author":"Naveed Humza","year":"2023","unstructured":"Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2023. A comprehensive overview of large language models. arXiv preprint arXiv:2307.06435 (2023)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2338"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0452"},{"key":"e_1_3_2_1_43_1","volume-title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300","author":"Shao Zhihong","year":"2024","unstructured":"Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al., 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 (2024)."},{"key":"e_1_3_2_1_44_1","volume-title":"GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks. arXiv preprint arXiv:2410.20147","author":"Takase Ryoichi","year":"2024","unstructured":"Ryoichi Takase, Masaya Tsunokake, Yuta Tsuchiya, and Shota Inuzuka. 2024. GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks. arXiv preprint arXiv:2410.20147 (2024)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.ijcnlp-long.180"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463032"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679569"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3069908"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679692"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679743"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591750"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512071"},{"key":"e_1_3_2_1_53_1","volume-title":"NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations. arXiv preprint arXiv:2511.18793","author":"Wang Yejing","year":"2025","unstructured":"Yejing Wang, Shengyu Zhou, Jinyu Lu, Ziwei Liu, Langming Liu, Maolin Wang, Wenlin Zhang, Feng Li, Wenbo Su, Pengjie Wang, et al., 2025b. NEZHA: A Zero-sacrifice and Hyperspeed Decoding Architecture for Generative Recommendations. arXiv preprint arXiv:2511.18793 (2025)."},{"key":"e_1_3_2_1_54_1","unstructured":"Zhipeng Wei Kuo Cai Junda She Jie Chen Minghao Chen Yang Zeng Qiang Luo Wencong Zeng Ruiming Tang Kun Gai et al. 2025. OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service. arXiv preprint arXiv:2508.14646 (2025)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3386195"},{"key":"e_1_3_2_1_56_1","volume-title":"Flow of reasoning: Efficient training of llm policy with divergent thinking. arXiv preprint arXiv:2406.05673","author":"Yu Fangxu","year":"2024","unstructured":"Fangxu Yu, Lai Jiang, Haoqiang Kang, Shibo Hao, and Lianhui Qin. 2024. Flow of reasoning: Efficient training of llm policy with divergent thinking. arXiv preprint arXiv:2406.05673 (2024)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709440"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33456"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219886"},{"key":"e_1_3_2_1_60_1","volume-title":"Deep Reinforcement Learning for List-wise Recommendations. arXiv preprint arXiv:1801.00209","author":"Zhao Xiangyu","year":"2017","unstructured":"Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Dawei Yin, Yihong Zhao, and Jiliang Tang. 2017. Deep Reinforcement Learning for List-wise Recommendations. arXiv preprint arXiv:1801.00209 (2017)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00118"},{"key":"e_1_3_2_1_62_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_63_1","volume-title":"EGA: A Unified End-to-End Generative Framework for Industrial Advertising Systems. arXiv preprint arXiv:2505.17549","author":"Zheng Zuowu","year":"2025","unstructured":"Zuowu Zheng, Ze Wang, Fan Yang, Jiangke Fan, Teng Zhang, and Xingxing Wang. 2025b. EGA: A Unified End-to-End Generative Framework for Industrial Advertising Systems. arXiv preprint arXiv:2505.17549 (2025)."},{"key":"e_1_3_2_1_64_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)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688178"}],"event":{"name":"SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:24:54Z","timestamp":1784136294000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805712.3809663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"references-count":65,"alternative-id":["10.1145\/3805712.3809663","10.1145\/3805712"],"URL":"https:\/\/doi.org\/10.1145\/3805712.3809663","relation":{},"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"2026-07-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}