{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:06:52Z","timestamp":1757311612783,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671593","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"4939-4950","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5562-1962","authenticated-orcid":false,"given":"Ariel","family":"Evnine","sequence":"first","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-4751","authenticated-orcid":false,"given":"Stratis","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"Northeastern University, Boston, MA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-2150","authenticated-orcid":false,"given":"Dimitris","family":"Kalimeris","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-5609","authenticated-orcid":false,"given":"Shankar","family":"Kalyanaraman","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7402-6874","authenticated-orcid":false,"given":"Weiwei","family":"Li","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8698-4730","authenticated-orcid":false,"given":"Israel","family":"Nir","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9066-3403","authenticated-orcid":false,"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6966-1945","authenticated-orcid":false,"given":"Udi","family":"Weinsberg","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Parag Agrawal. 2024. Building a Large-Scale Recommendation System: People You May Know. https:\/\/www.linkedin.com\/blog\/engineering\/recommendations\/ building-a-large-scale-recommendation-system-people-you-may-know"},{"key":"e_1_3_2_2_2_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 3400--3409","author":"Agrawal Raj","year":"2019","unstructured":"Raj Agrawal, Chandler Squires, Karren Yang, Karthikeyan Shanmugam, and Caroline Uhler. 2019. Abcd-strategy: Budgeted experimental design for targeted causal structure discovery. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 3400--3409."},{"key":"e_1_3_2_2_3_1","unstructured":"Rohan Anil Sandra Gadanho Da Huang Nijith Jacob Zhuoshu Li Dong Lin Todd Phillips Cristina Pop Kevin Regan Gil I Shamir et al. 2022. On the factory floor: ML engineering for industrial-scale ads recommendation models. arXiv preprint arXiv:2209.05310 (2022)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484132"},{"key":"e_1_3_2_2_5_1","volume-title":"Continuous DR-submodular maximization: Structure and algorithms. Advances in Neural Information Processing Systems 30","author":"Bian An","year":"2017","unstructured":"An Bian, Kfir Levy, Andreas Krause, and Joachim M Buhmann. 2017. Continuous DR-submodular maximization: Structure and algorithms. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2634074.2634144"},{"key":"e_1_3_2_2_7_1","volume-title":"Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems. arXiv preprint arXiv:2209.05000","author":"Bower Amanda","year":"2022","unstructured":"Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, and Luca Belli. 2022. Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems. arXiv preprint arXiv:2209.05000 (2022)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/956863.956944"},{"key":"e_1_3_2_2_9_1","volume-title":"Constrained Submodular Maximization via New Bounds for DR-Submodular Functions. arXiv preprint arXiv:2311.01129","author":"Buchbinder Niv","year":"2023","unstructured":"Niv Buchbinder and Moran Feldman. 2023. Constrained Submodular Maximization via New Bounds for DR-Submodular Functions. arXiv preprint arXiv:2311.01129 (2023)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512014"},{"key":"e_1_3_2_2_11_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR","author":"Chen Lin","year":"2018","unstructured":"Lin Chen, Hamed Hassani, and Amin Karbasi. 2018. Online continuous submodular maximization. In International Conference on Artificial Intelligence and Statistics. PMLR, 1896--1905."},{"key":"e_1_3_2_2_12_1","volume-title":"Combinatorial multiarmed bandit with general reward functions. Advances in Neural Information Processing Systems 29","author":"Chen Wei","year":"2016","unstructured":"Wei Chen,Wei Hu, Fu Li, Jian Li, Yu Liu, and Pinyan Lu. 2016. Combinatorial multiarmed bandit with general reward functions. Advances in Neural Information Processing Systems 29 (2016)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-016-9279-1"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557056"},{"key":"e_1_3_2_2_17_1","volume-title":"On Ranking Consistency of Pre-ranking Stage Consistency of Pre-ranking Stage. arXiv preprint arXiv:2205.01289","author":"Gu Siyu","year":"2022","unstructured":"Siyu Gu, Xiang-Rong Sheng, Biye Jiang, Siyuan Lou, Shuguang Han, Hongbo Deng, and Bo Zheng. 2022. On Ranking Consistency of Pre-ranking Stage Consistency of Pre-ranking Stage. arXiv preprint arXiv:2205.01289 (2022)."},{"key":"e_1_3_2_2_18_1","volume-title":"On component interactions in two-stage recommender systems. Advances in neural information processing systems 34","author":"Hron Jiri","year":"2021","unstructured":"Jiri Hron, Karl Krauth, Michael Jordan, and Niki Kilbertus. 2021. On component interactions in two-stage recommender systems. Advances in neural information processing systems 34 (2021), 2744--2757."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583422"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_2_2_21_1","volume-title":"Stochastic submodular maximization: The case of coverage functions. Advances in Neural Information Processing Systems 30","author":"Karimi Mohammad","year":"2017","unstructured":"Mohammad Karimi, Mario Lucic, Hamed Hassani, and Andreas Krause. 2017. Stochastic submodular maximization: The case of coverage functions. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_3_2_2_22_1","first-page":"3","article-title":"Team performance with test scores","volume":"6","author":"Kleinberg Jon","year":"2018","unstructured":"Jon Kleinberg and Maithra Raghu. 2018. Team performance with test scores. ACM Transactions on Economics and Computation (TEAC) 6, 3--4 (2018), 1--26.","journal-title":"ACM Transactions on Economics and Computation (TEAC)"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139177801.004"},{"key":"e_1_3_2_2_24_1","unstructured":"Lada Akos and Wang Meihong and Yan Tak. 2021. How does News Feed predict what you want to see? https:\/\/tech.facebook.com\/engineering\/2021\/01\/newsfeed-ranking\/"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/771"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380130"},{"key":"e_1_3_2_2_28_1","volume-title":"International Conference on Machine Learning. PMLR, 428--437","author":"Maehara Takanori","year":"2015","unstructured":"Takanori Maehara, Akihiro Yabe, and Ken-ichi Kawarabayashi. 2015. Budget allocation problem with multiple advertisers: A game theoretic view. In International Conference on Machine Learning. PMLR, 428--437."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583195"},{"key":"e_1_3_2_2_30_1","first-page":"15800","article-title":"Hitting the high notes: Subset selection for maximizing expected order statistics","volume":"33","author":"Mehta Aranyak","year":"2020","unstructured":"Aranyak Mehta, Uri Nadav, Alexandros Psomas, and Aviad Rubinstein. 2020. Hitting the high notes: Subset selection for maximizing expected order statistics. Advances in Neural Information Processing Systems 33 (2020), 15800--15810.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_31_1","unstructured":"Meta Inc. 2023. Facebook People You May Know AI system. https:\/\/transparency. fb.com\/features\/explaining-ranking\/fb-people-you-may-know\/"},{"key":"e_1_3_2_2_32_1","unstructured":"Meta Inc. 2023. Instagram Suggested Accounts AI system. https:\/\/transparency.fb. com\/features\/explaining-ranking\/ig-suggested-accounts\/"},{"key":"e_1_3_2_2_33_1","unstructured":"Meta Inc. 2023. Scaling the Instagram Explore recommendations system. https:\/\/engineering.fb.com\/2023\/08\/09\/ml-applications\/scaling-instagramexplore-recommendations-system\/"},{"key":"e_1_3_2_2_34_1","volume-title":"International Conference on Machine Learning. PMLR, 3596--3605","author":"Mitrovic Marko","year":"2018","unstructured":"Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, and Amin Karbasi. 2018. Data summarization at scale: A two-stage submodular approach. In International Conference on Machine Learning. PMLR, 3596--3605."},{"key":"e_1_3_2_2_35_1","volume-title":"International Conference on Machine Learning. PMLR, 26495--26516","author":"Okati Nastaran","year":"2023","unstructured":"Nastaran Okati, Stratis Tsirtsis, and Manuel Gomez Rodriguez. 2023. On the within-group fairness of screening classifiers. In International Conference on Machine Learning. PMLR, 26495--26516."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532050"},{"key":"e_1_3_2_2_37_1","volume-title":"IN: ICML 2020 workshop on Negative Dependence and Submodularity for ML","volume":"119","author":"Sahin Aytunc","year":"2020","unstructured":"Aytunc Sahin, Joachim Buhmann, and Andreas Krause. 2020. Constrained maximization of lattice submodular functions. In IN: ICML 2020 workshop on Negative Dependence and Submodularity for ML, Vienna, Austria, PMLR, Vol. 119."},{"key":"e_1_3_2_2_38_1","volume-title":"Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice. arXiv preprint arXiv:2111.10175","author":"Schiabel Alberto","year":"2021","unstructured":"Alberto Schiabel, Vyacheslav Kungurtsev, and Jakub Marecek. 2021. Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice. arXiv preprint arXiv:2111.10175 (2021)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3465456.3467614"},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Machine Learning. PMLR, 351--359","author":"Soma Tasuku","year":"2014","unstructured":"Tasuku Soma, Naonori Kakimura, Kazuhiro Inaba, and Ken-ichi Kawarabayashi. 2014. Optimal budget allocation: Theoretical guarantee and efficient algorithm. In International Conference on Machine Learning. PMLR, 351--359."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10653"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3288898.3288960"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557683"},{"key":"e_1_3_2_2_44_1","volume-title":"Submodularity in combinatorial optimization","author":"Vondr\u00e1k Jan","year":"2007","unstructured":"Jan Vondr\u00e1k. 2007. Submodularity in combinatorial optimization. Charles University, Prague (2007)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570469"},{"key":"e_1_3_2_2_47_1","volume-title":"International Conference on Machine Learning. PMLR, 22702--22726","author":"Wang Lequn","year":"2022","unstructured":"Lequn Wang, Thorsten Joachims, and Manuel Gomez Rodriguez. 2022. Improving screening processes via calibrated subset selection. In International Conference on Machine Learning. PMLR, 22702--22726."},{"key":"e_1_3_2_2_48_1","unstructured":"Xuewei Wang Qiang Jin Shengyu Huang Min Zhang Xi Liu Zhengli Zhao Yukun Chen Zhengyu Zhang Jiyan Yang Ellie Wen Sagar Chordia Wenlin Chen and Qin Huang. 2023. Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking. arXiv:2307.11096 [cs.IR]"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3346996"},{"key":"e_1_3_2_2_50_1","volume-title":"Ranking with submodular functions on a budget. Data mining and knowledge discovery 36, 3","author":"Zhang Guangyi","year":"2022","unstructured":"Guangyi Zhang, Nikolaj Tatti, and Aristides Gionis. 2022. Ranking with submodular functions on a budget. Data mining and knowledge discovery 36, 3 (2022), 1197--1218."},{"key":"e_1_3_2_2_51_1","volume-title":"Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System. arXiv preprint arXiv:2305.13647","author":"Zhang Zhixuan","year":"2023","unstructured":"Zhixuan Zhang, Yuheng Huang, Dan Ou, Sen Li, Longbin Li, Qingwen Liu, and Xiaoyi Zeng. 2023. Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System. arXiv preprint arXiv:2305.13647 (2023)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Barcelona Spain","acronym":"KDD '24"},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671593","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671593","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:20Z","timestamp":1750291460000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":51,"alternative-id":["10.1145\/3637528.3671593","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671593","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}