{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:32:05Z","timestamp":1759883525314,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"DOI":"10.1145\/3701716.3715514","type":"proceedings-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:06:11Z","timestamp":1748016371000},"page":"1162-1166","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Large Memory Network for Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5133-4452","authenticated-orcid":false,"given":"Hui","family":"Lu","sequence":"first","affiliation":[{"name":"ByteDance, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9823-2990","authenticated-orcid":false,"given":"Zheng","family":"Chai","sequence":"additional","affiliation":[{"name":"ByteDance, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9804-2721","authenticated-orcid":false,"given":"Yuchao","family":"Zheng","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2716-9885","authenticated-orcid":false,"given":"Zhe","family":"Chen","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7412-3536","authenticated-orcid":false,"given":"Deping","family":"Xie","sequence":"additional","affiliation":[{"name":"ByteDance, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2580-0983","authenticated-orcid":false,"given":"Peng","family":"Xu","sequence":"additional","affiliation":[{"name":"ByteDance, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6788-8978","authenticated-orcid":false,"given":"Xun","family":"Zhou","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8084-7608","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498435"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557082"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532073"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599922"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3474255"},{"key":"e_1_3_2_1_6_1","volume-title":"Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).","author":"Girshick R","year":"2015","unstructured":"R Girshick. 2015. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV)."},{"key":"e_1_3_2_1_7_1","volume-title":"Forty-first International Conference on Machine Learning.","author":"Guo Xingzhuo","year":"2024","unstructured":"Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, and Mingsheng Long. 2024. On the Embedding Collapse when Scaling up Recommendation Models. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.57"},{"key":"e_1_3_2_1_9_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Lample Guillaume","year":"2019","unstructured":"Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato, Ludovic Denoyer, and Herv\u00e9 J\u00e9gou. 2019. Large memory layers with product keys. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_10_1","unstructured":"Qi Liu Xuyang Hou Haoran Jin Zhe Wang Defu Lian Tan Qu Jia Cheng Jun Lei et al. 2023. Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction. arXiv preprint arXiv:2311.10764 (2023)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1147"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330666"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412744"},{"key":"e_1_3_2_1_14_1","volume-title":"International conference on machine learning. PMLR, 802--810","author":"Yan Ling","year":"2014","unstructured":"Ling Yan, Wu-Jun Li, Gui-Rong Xue, and Dingyi Han. 2014. Coupled group lasso for web-scale ctr prediction in display advertising. In International conference on machine learning. PMLR, 802--810."},{"key":"e_1_3_2_1_15_1","volume-title":"IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling. arXiv preprint arXiv:2406.09742","author":"Yu Wenhui","year":"2024","unstructured":"Wenhui Yu, Chao Feng, Yanze Zhang, Lantao Hu, Peng Jiang, and Han Li. 2024. IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling. arXiv preprint arXiv:2406.09742 (2024)."},{"volume-title":"International conference on machine learning.","author":"Zhai Jiaqi","key":"e_1_3_2_1_16_1","unstructured":"Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Jiayuan He, et al. [n.,d.]. Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations. In International conference on machine learning."},{"volume-title":"Forty-first International Conference on Machine Learning.","author":"Zhang Buyun","key":"e_1_3_2_1_17_1","unstructured":"Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, et al. [n.,d.]. Wukong: Towards a Scaling Law for Large-Scale Recommendation. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Sydney NSW Australia","acronym":"WWW '25"},"container-title":["Companion Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715514","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701716.3715514","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T17:49:08Z","timestamp":1759859348000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":19,"alternative-id":["10.1145\/3701716.3715514","10.1145\/3701716"],"URL":"https:\/\/doi.org\/10.1145\/3701716.3715514","relation":{},"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}