{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:10:14Z","timestamp":1782450614025,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"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":[[2022,8,14]]},"DOI":"10.1145\/3534678.3542598","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"4784-4785","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Graph-based Representation Learning for Web-scale Recommender Systems"],"prefix":"10.1145","author":[{"given":"Ahmed","family":"El-Kishky","sequence":"first","affiliation":[{"name":"Twitter Cortex, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Bronstein","sequence":"additional","affiliation":[{"name":"Twitter Cortex, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xiao","sequence":"additional","affiliation":[{"name":"Twitter Cortex, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aria","family":"Haghighi","sequence":"additional","affiliation":[{"name":"Twitter Cortex, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"A. Bordes N. Usunier A. Garcia-Duran J. Weston and O. Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. NeurIPS (2013)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"S. Cao W. Lu and Q. Xu. 2016. Deep neural networks for learning graph representations. In AAAI.","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"S. Chang W. Han J. Tang G. Qi C. Aggarwal and T. Huang. 2015. Heterogeneous network embedding via deep architectures. In KDD.","DOI":"10.1145\/2783258.2783296"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"P. Covington J. Adams and E. Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_6_1","volume-title":"2022 a. kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval. arXiv preprint arXiv:2205.06205","author":"El-Kishky A.","year":"2022","unstructured":"A. El-Kishky, T. Markovich, K. Leung, F. Portman, A. Haghighi, and Y. Xiao. 2022 a. kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval. arXiv preprint arXiv:2205.06205 (2022)."},{"key":"e_1_3_2_1_7_1","volume-title":"2022 b. TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation. KDD","author":"El-Kishky A.","year":"2022","unstructured":"A. El-Kishky, T. Markovich, S. Park, C. Verma, B. Kim, R. Eskander, Y. Malkov, F. Portman, S. Samaniego, et al. 2022 b. TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation. KDD (2022)."},{"key":"e_1_3_2_1_8_1","volume":"201","author":"Grover A.","unstructured":"A. Grover and J. Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD.","journal-title":"J. Leskovec."},{"key":"e_1_3_2_1_9_1","volume":"201","author":"Hamilton W","unstructured":"W Hamilton, Z. Ying, and J. Leskovec. 2017. Inductive representation learning on large graphs. NeurIPS (2017).","journal-title":"J. Leskovec."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"W. Kang D. Cheng T. Yao X. Yi T. Chen L. Hong and E. Chi. 2020. Learning to embed categorical features without embedding tables for recommendation. arXiv:2010.10784 (2020).","DOI":"10.1145\/3447548.3467304"},{"key":"e_1_3_2_1_11_1","volume-title":"et almbox","author":"Naumov M.","year":"2019","unstructured":"M. Naumov, D. Mudigere, M. Shi, J. Huang, N. Sundaraman, J. Park, X. Wang, U. Gupta, C. Wu, A. Azzolini, et almbox. 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv:1906.00091 (2019)."},{"key":"e_1_3_2_1_12_1","volume":"202","author":"Pal A.","unstructured":"A. Pal, C. Eksombatchai, Y. Zhou, B. Zhao, C. Rosenberg, and J. Leskovec. 2020. Pinnersage: Multi-modal user embedding framework for recommendations at pinterest. In KDD.","journal-title":"J. Leskovec."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"J. Pougu\u00e9-Biyong A. Gupta A. Haghighi and A. El-Kishky. 2022. Learning Stance Embeddings from Signed Social Graphs. arXiv preprint arXiv:2201.11675 (2022).","DOI":"10.1145\/3539597.3570401"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_16_1","unstructured":"T. Trouillon J. Welbl S. Riedel \u00c9. Gaussier and G. Bouchard. 2016. Complex embeddings for simple link prediction. In ICML. PMLR."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"D. Wang P. Cui and W. Zhu. 2016. Structural deep network embedding. In KDD.","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3124749.3124754"},{"key":"e_1_3_2_1_19_1","unstructured":"S. Wu F. Sun W. Zhang and B. Cui. 2020. Graph neural networks in recommender systems: a survey. arXiv:2011.02260 (2020)."},{"key":"e_1_3_2_1_20_1","unstructured":"B. Yang W. Yih X. He J. Gao and L. Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv:1412.6575 (2014)."},{"key":"e_1_3_2_1_21_1","unstructured":"C. Yang Z. Liu D. Zhao M. Sun and E. Chang. 2015. Network representation learning with rich text information. In IJCAI."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542598","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3542598","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:04Z","timestamp":1750186984000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3542598"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":21,"alternative-id":["10.1145\/3534678.3542598","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3542598","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}