{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T15:37:05Z","timestamp":1759937825273,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":14,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"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":[[2024,10,8]]},"DOI":"10.1145\/3640457.3688053","type":"proceedings-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T15:39:28Z","timestamp":1728401968000},"page":"838-840","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3764-3643","authenticated-orcid":false,"given":"Yi-Ping","family":"Hsu","sequence":"first","affiliation":[{"name":"Pinterest, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0662-1312","authenticated-orcid":false,"given":"Po-Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Pinterest, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7855-4594","authenticated-orcid":false,"given":"Chantat","family":"Eksombatchai","sequence":"additional","affiliation":[{"name":"Pinterest, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4761-5171","authenticated-orcid":false,"given":"Jiajing","family":"Xu","sequence":"additional","affiliation":[{"name":"Pinterest, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Rohan Anil Sandra Gadanho Da Huang Nijith Jacob Zhuoshu Li Dong Lin Todd Phillips Cristina Pop Kevin Regan Gil\u00a0I. Shamir Rakesh Shivanna and Qiqi Yan. 2022. On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. arxiv:2209.05310\u00a0[cs.IR]"},{"key":"e_1_3_2_1_2_1","volume-title":"Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction. arXiv preprint arXiv:2209.12212","author":"Chen Qiwei","year":"2022","unstructured":"Qiwei Chen, Yue Xu, Changhua Pei, Shanshan Lv, Tao Zhuang, and Junfeng Ge. 2022. Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction. arXiv preprint arXiv:2209.12212 (2022)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_4_1","volume-title":"On power-law relationships of the internet topology. ACM SIGCOMM computer communication review 29, 4","author":"Faloutsos Michalis","year":"1999","unstructured":"Michalis Faloutsos, Petros Faloutsos, and Christos Faloutsos. 1999. On power-law relationships of the internet topology. ACM SIGCOMM computer communication review 29, 4 (1999), 251\u2013262."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081893"},{"key":"e_1_3_2_1_6_1","volume-title":"Monolith: real time recommendation system with collisionless embedding table. arXiv preprint arXiv:2209.07663","author":"Liu Zhuoran","year":"2022","unstructured":"Zhuoran Liu, Leqi Zou, Xuan Zou, Caihua Wang, Biao Zhang, Da Tang, Bolin Zhu, Yijie Zhu, Peng Wu, Ke Wang, 2022. Monolith: real time recommendation system with collisionless embedding table. arXiv preprint arXiv:2209.07663 (2022)."},{"key":"e_1_3_2_1_7_1","volume-title":"Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091","author":"Naumov Maxim","year":"2019","unstructured":"Maxim Naumov, Dheevatsa Mudigere, Hao-Jun\u00a0Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson\u00a0G Azzolini, 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539156"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330666"},{"key":"e_1_3_2_1_10_1","volume-title":"Chi, and Maheswaran Sathiamoorthy","author":"Singh Anima","year":"2023","unstructured":"Anima Singh, Trung Vu, Raghunandan Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed Chi, and Maheswaran Sathiamoorthy. 2023. Better Generalization with Semantic IDs: A case study in Ranking for Recommendations. arXiv preprint arXiv:2306.08121 (2023)."},{"key":"e_1_3_2_1_11_1","first-page":"448","article-title":"Tt-rec: Tensor train compression for deep learning recommendation models","volume":"3","author":"Yin Chunxing","year":"2021","unstructured":"Chunxing Yin, Bilge Acun, Carole-Jean Wu, and Xing Liu. 2021. Tt-rec: Tensor train compression for deep learning recommendation models. Proceedings of Machine Learning and Systems 3 (2021), 448\u2013462.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557479"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482486"}],"event":{"name":"RecSys '24: 18th ACM Conference on Recommender Systems","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval","SIGCHI ACM Special Interest Group on Computer-Human Interaction"],"location":"Bari Italy","acronym":"RecSys '24"},"container-title":["18th ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640457.3688053","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3640457.3688053","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:29Z","timestamp":1750294709000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640457.3688053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":14,"alternative-id":["10.1145\/3640457.3688053","10.1145\/3640457"],"URL":"https:\/\/doi.org\/10.1145\/3640457.3688053","relation":{},"subject":[],"published":{"date-parts":[[2024,10,8]]},"assertion":[{"value":"2024-10-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}