{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:11:52Z","timestamp":1784178712005,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"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":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482362","type":"proceedings-article","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T18:33:11Z","timestamp":1635618791000},"page":"524-533","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Learning An End-to-End Structure for Retrieval in Large-Scale Recommendations"],"prefix":"10.1145","author":[{"given":"Weihao","family":"Gao","sequence":"first","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangjun","family":"Fan","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Wang","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Bellevue, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiankai","family":"Sun","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Bellevue, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Jia","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzi","family":"Xiao","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruofan","family":"Ding","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyan","family":"Bin","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobing","family":"Liu","sequence":"additional","affiliation":[{"name":"ByteDance Inc., Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Martin Renqiang Min, and Yizhou Sun","author":"Chen Ting","year":"2018","unstructured":"Ting Chen , Martin Renqiang Min, and Yizhou Sun . 2018 . Learning K-way D-dimensional discrete codes for compact embedding representations. arXiv preprint arXiv:1806.09464 (2018). Ting Chen, Martin Renqiang Min, and Yizhou Sun. 2018. Learning K-way D-dimensional discrete codes for compact embedding representations. arXiv preprint arXiv:1806.09464 (2018)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104359"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327757.3327825"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.240"},{"key":"e_1_3_2_2_7_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).  Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_11_1","volume-title":"Rank-based similarity search: Reducing the dimensional dependence","author":"Houle Michael E","year":"2014","unstructured":"Michael E Houle and Michael Nett . 2014. Rank-based similarity search: Reducing the dimensional dependence . IEEE transactions on pattern analysis and machine intelligence, Vol. 37 , 1 ( 2014 ), 136--150. Michael E Houle and Michael Nett. 2014. Rank-based similarity search: Reducing the dimensional dependence. IEEE transactions on pattern analysis and machine intelligence, Vol. 37, 1 (2014), 136--150."},{"key":"e_1_3_2_2_12_1","volume-title":"Log-time and log-space extreme classification. arXiv preprint arXiv:1611.01964","author":"Jasinska Kalina","year":"2016","unstructured":"Kalina Jasinska and Nikos Karampatziakis . 2016. Log-time and log-space extreme classification. arXiv preprint arXiv:1611.01964 ( 2016 ). Kalina Jasinska and Nikos Karampatziakis. 2016. Log-time and log-space extreme classification. arXiv preprint arXiv:1611.01964 (2016)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.57"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357930"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2003.1167344"},{"key":"e_1_3_2_2_18_1","volume-title":"Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs","author":"Malkov Yury A","year":"2018","unstructured":"Yury A Malkov and Dmitry A Yashunin . 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs . IEEE transactions on pattern analysis and machine intelligence ( 2018 ). Yury A Malkov and Dmitry A Yashunin. 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence (2018)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455475"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30570-5_27"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/2981562.2981720"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2321376"},{"key":"e_1_3_2_2_24_1","volume-title":"Department of Computer Science. Camegie-Mell University","volume":"17","author":"Raj D","year":"1977","unstructured":"D Raj Reddy et al. 1977. Speech understanding systems: A summary of results of the five-year research effort . Department of Computer Science. Camegie-Mell University , Pittsburgh, PA , Vol. 17 ( 1977 ). D Raj Reddy et al. 1977. Speech understanding systems: A summary of results of the five-year research effort. Department of Computer Science. Camegie-Mell University, Pittsburgh, PA, Vol. 17 (1977)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969086"},{"key":"e_1_3_2_2_28_1","volume-title":"A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models. arXiv preprint arXiv:1703.05160","author":"Spring Ryan","year":"2017","unstructured":"Ryan Spring and Anshumali Shrivastava . 2017. A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models. arXiv preprint arXiv:1703.05160 ( 2017 ). Ryan Spring and Anshumali Shrivastava. 2017. A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models. arXiv preprint arXiv:1703.05160 (2017)."},{"key":"e_1_3_2_2_29_1","unstructured":"Aaron Van Den Oord Oriol Vinyals etal 2017. Neural discrete representation learning. (2017) 6306--6315.  Aaron Van Den Oord Oriol Vinyals et al. 2017. Neural discrete representation learning. (2017) 6306--6315."},{"key":"e_1_3_2_2_30_1","volume-title":"Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al.","author":"Yao Tiansheng","year":"2020","unstructured":"Tiansheng Yao , Xinyang Yi , Derek Zhiyuan Cheng , Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al. 2020 . Self-supervised Learning for Large-scale Item Recommendations . arXiv preprint arXiv:2007.12865 (2020). Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H Chi, Steve Tjoa, Jieqi Kang, et al. 2020. Self-supervised Learning for Large-scale Item Recommendations. arXiv preprint arXiv:2007.12865 (2020)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454810"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467102"},{"key":"e_1_3_2_2_33_1","unstructured":"Han Zhu Daqing Chang Ziru Xu Pengye Zhang Xiang Li Jie He Han Li Jian Xu and Kun Gai. 2019. Joint optimization of tree-based index and deep model for recommender systems. In Advances in Neural Information Processing Systems. 3973--3982.  Han Zhu Daqing Chang Ziru Xu Pengye Zhang Xiang Li Jie He Han Li Jian Xu and Kun Gai. 2019. Joint optimization of tree-based index and deep model for recommender systems. In Advances in Neural Information Processing Systems. 3973--3982."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219826"},{"key":"e_1_3_2_2_35_1","volume-title":"Learning optimal tree models under beam search. arxiv","author":"Zhuo Jingwei","year":"2006","unstructured":"Jingwei Zhuo , Ziru Xu , Wei Dai , Han Zhu , Han Li , Jian Xu , and Kun Gai . 2020. Learning optimal tree models under beam search. arxiv : 2006 .15408 [stat.ML] Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, and Kun Gai. 2020. Learning optimal tree models under beam search. arxiv: 2006.15408 [stat.ML]"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482362","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482362","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:23Z","timestamp":1750191143000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482362"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":35,"alternative-id":["10.1145\/3459637.3482362","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482362","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}