{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:35:40Z","timestamp":1773192940727,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":8,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,22]]},"DOI":"10.1145\/3705328.3748143","type":"proceedings-article","created":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T10:48:44Z","timestamp":1757155724000},"page":"1058-1061","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential Transducers"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8781-2384","authenticated-orcid":false,"given":"Yue","family":"Dong","sequence":"first","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4818-2759","authenticated-orcid":false,"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8484-0166","authenticated-orcid":false,"given":"Shen","family":"Li","sequence":"additional","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0369-5058","authenticated-orcid":false,"given":"Nikhil","family":"Patel","sequence":"additional","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0172-8698","authenticated-orcid":false,"given":"Xing","family":"Liu","sequence":"additional","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5436-9952","authenticated-orcid":false,"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3325-991X","authenticated-orcid":false,"given":"Chuanhao","family":"Zhuge","sequence":"additional","affiliation":[{"name":"Meta Platforms, Menlo Park, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,9,7]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"Jianxin Chang Chenbin Zhang Zhiyi Fu Xiaoxue Zang Lin Guan Jing Lu Yiqun Hui Dewei Leng Yanan Niu Yang Song and Kun Gai. 2023. TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou. arxiv:https:\/\/arXiv.org\/abs\/2302.02352\u00a0[cs.IR] https:\/\/arxiv.org\/abs\/2302.02352"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3547387"},{"key":"e_1_3_3_1_4_2","unstructured":"Dacheng Li Rulin Shao Anze Xie Eric\u00a0P. Xing Xuezhe Ma Ion Stoica Joseph\u00a0E. Gonzalez and Hao Zhang. 2024. DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training. arxiv:https:\/\/arXiv.org\/abs\/2310.03294\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2310.03294"},{"key":"e_1_3_3_1_5_2","unstructured":"Hao Liu Matei Zaharia and Pieter Abbeel. 2023. Ring Attention with Blockwise Transformers for Near-Infinite Context. arxiv:https:\/\/arXiv.org\/abs\/2310.01889\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2310.01889"},{"key":"e_1_3_3_1_6_2","unstructured":"NVIDIA Corporation. 2025. TransformerEngine: High-Performance Transformer Primitives for NVIDIA GPUs. https:\/\/github.com\/NVIDIA\/TransformerEngine. GitHub repository commit 9b2fed5 accessed 15\u00a0May\u00a02025."},{"key":"e_1_3_3_1_7_2","unstructured":"Nikil Pancha Andrew Zhai Jure Leskovec and Charles Rosenberg. 2022. PinnerFormer: Sequence Modeling for User Representation at Pinterest. arxiv:https:\/\/arXiv.org\/abs\/2205.04507\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2205.04507"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. arxiv:https:\/\/arXiv.org\/abs\/1904.06690\u00a0[cs.IR] https:\/\/arxiv.org\/abs\/1904.06690","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_3_1_9_2","unstructured":"Jiaqi Zhai Lucy Liao Xing Liu Yueming Wang Rui Li Xuan Cao Leon Gao Zhaojie Gong Fangda Gu Michael He Yinghai Lu and Yu Shi. 2024. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations. arxiv:https:\/\/arXiv.org\/abs\/2402.17152\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2402.17152"}],"event":{"name":"RecSys '25: Nineteenth ACM Conference on Recommender Systems","location":"Prague Czech Republic","acronym":"RecSys '25","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"]},"container-title":["Proceedings of the Nineteenth ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705328.3748143","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:51:47Z","timestamp":1757159507000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705328.3748143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,7]]},"references-count":8,"alternative-id":["10.1145\/3705328.3748143","10.1145\/3705328"],"URL":"https:\/\/doi.org\/10.1145\/3705328.3748143","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"}}]}}