{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:01:45Z","timestamp":1768006905279,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,13]]},"DOI":"10.1145\/3460231.3474257","type":"proceedings-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T21:45:04Z","timestamp":1631569504000},"page":"432-442","source":"Crossref","is-referenced-by-count":23,"title":["A Payload Optimization Method for Federated Recommender Systems"],"prefix":"10.1145","author":[{"given":"Farwa K.","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science National University of Computer and Emerging Sciences (Pakistan), Pakistan"}]},{"given":"Adrian","family":"Flanagan","sequence":"additional","affiliation":[{"name":"Helsinki Research Center, Europe Cloud Service Competence Center, Huawei Technologies Oy (Finland) Co. Ltd, Finland"}]},{"given":"Kuan Eeik","family":"Tan","sequence":"additional","affiliation":[{"name":"Helsinki Research Center, Europe Cloud Service Competence Center, Huawei Technologies Oy (Finland) Co. Ltd, Finland"}]},{"given":"Zareen","family":"Alamgir","sequence":"additional","affiliation":[{"name":"Department of Computer Science National University of Computer and Emerging Technology (Pakistan), Pakistan"}]},{"given":"Muhammad","family":"Ammad-ud-din","sequence":"additional","affiliation":[{"name":"Helsinki Research Center, Europe Cloud Service Competence Center, Huawei Technologies Oy (Finland) Co. Ltd and Comparables AI (Finland) Co. Ltd, Finland"}]}],"member":"320","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Shipra Agrawal and Navin Goyal. 2013. Further optimal regret bounds for thompson sampling. In Artificial intelligence and statistics. PMLR 99\u2013107.  Shipra Agrawal and Navin Goyal. 2013. Further optimal regret bounds for thompson sampling. In Artificial intelligence and statistics. PMLR 99\u2013107."},{"key":"e_1_3_2_2_2_1","unstructured":"Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman\u00a0A Khan Were Oyomno Qiang Fu Kuan\u00a0Eeik Tan and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888(2019).  Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman\u00a0A Khan Were Oyomno Qiang Fu Kuan\u00a0Eeik Tan and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888(2019)."},{"key":"e_1_3_2_2_3_1","volume-title":"Recommender systems survey. Knowledge-based systems 46","author":"Bobadilla Jes\u00fas","year":"2013","unstructured":"Jes\u00fas Bobadilla , Fernando Ortega , Antonio Hernando , and Abraham Guti\u00e9rrez . 2013. Recommender systems survey. Knowledge-based systems 46 ( 2013 ), 109\u2013132. Jes\u00fas Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Guti\u00e9rrez. 2013. Recommender systems survey. Knowledge-based systems 46 (2013), 109\u2013132."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3172944.3172967"},{"key":"e_1_3_2_2_5_1","unstructured":"Sebastian Caldas Jakub Kone\u010dny H\u00a0Brendan McMahan and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210(2018).  Sebastian Caldas Jakub Kone\u010dny H\u00a0Brendan McMahan and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210(2018)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2039320"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.3014880"},{"key":"e_1_3_2_2_8_1","volume-title":"An empirical evaluation of thompson sampling. Advances in neural information processing systems 24","author":"Chapelle Olivier","year":"2011","unstructured":"Olivier Chapelle and Lihong Li. 2011. An empirical evaluation of thompson sampling. Advances in neural information processing systems 24 ( 2011 ), 2249\u20132257. Olivier Chapelle and Lihong Li. 2011. An empirical evaluation of thompson sampling. Advances in neural information processing systems 24 (2011), 2249\u20132257."},{"key":"e_1_3_2_2_9_1","unstructured":"Fei Chen Zhenhua Dong Zhenguo Li and Xiuqiang He. 2018. Federated meta-learning for recommendation. arXiv preprint arXiv:1802.07876(2018).  Fei Chen Zhenhua Dong Zhenguo Li and Xiuqiang He. 2018. Federated meta-learning for recommendation. arXiv preprint arXiv:1802.07876(2018)."},{"key":"e_1_3_2_2_10_1","volume-title":"Bryan Kian\u00a0Hsiang Low, and Patrick Jaillet","author":"Dai Zhongxiang","year":"2020","unstructured":"Zhongxiang Dai , Bryan Kian\u00a0Hsiang Low, and Patrick Jaillet . 2020 . Federated Bayesian Optimization via Thompson Sampling. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.), Vol.\u00a033. Curran Associates, Inc ., 9687\u20139699. Zhongxiang Dai, Bryan Kian\u00a0Hsiang Low, and Patrick Jaillet. 2020. Federated Bayesian Optimization via Thompson Sampling. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.), Vol.\u00a033. Curran Associates, Inc., 9687\u20139699."},{"key":"e_1_3_2_2_11_1","volume-title":"The 17th Annual International Conference on Mobile Systems, Applications, and Services. 624\u2013625","author":"Dolui Koustabh","year":"2019","unstructured":"Koustabh Dolui , Illapha\u00a0Cuba Gyllensten , Dietwig Lowet , Sam Michiels , Hans Hallez , and Danny Hughes . 2019 . Poster: Towards Privacy-preserving Mobile Applications with Federated Learning\u2013The Case of Matrix Factorization . In The 17th Annual International Conference on Mobile Systems, Applications, and Services. 624\u2013625 . Koustabh Dolui, Illapha\u00a0Cuba Gyllensten, Dietwig Lowet, Sam Michiels, Hans Hallez, and Danny Hughes. 2019. Poster: Towards Privacy-preserving Mobile Applications with Federated Learning\u2013The Case of Matrix Factorization. In The 17th Annual International Conference on Mobile Systems, Applications, and Services. 624\u2013625."},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems. 4161\u20134169","author":"Dong Shi","year":"2018","unstructured":"Shi Dong and Benjamin\u00a0Van Roy . 2018 . An information-theoretic analysis for thompson sampling with many actions . In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 4161\u20134169 . Shi Dong and Benjamin\u00a0Van Roy. 2018. An information-theoretic analysis for thompson sampling with many actions. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 4161\u20134169."},{"key":"e_1_3_2_2_13_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.), Vol.\u00a033. Curran Associates","author":"Dubey Abhimanyu","unstructured":"Abhimanyu Dubey and Alex \u2035\u00a0Sandy'Pentland . 2020. Differentially-Private Federated Linear Bandits . In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.), Vol.\u00a033. Curran Associates , Inc ., 6003\u20136014. Abhimanyu Dubey and Alex \u2035\u00a0Sandy'Pentland. 2020. Differentially-Private Federated Linear Bandits. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.\u00a0F. Balcan, and H.\u00a0Lin (Eds.), Vol.\u00a033. Curran Associates, Inc., 6003\u20136014."},{"key":"e_1_3_2_2_14_1","unstructured":"Daniel Fink. 1997. A compendium of conjugate priors. See http:\/\/www. people. cornell. edu\/pages\/df36\/CONJINTRnew% 20TEX. pdf 46(1997).  Daniel Fink. 1997. A compendium of conjugate priors. See http:\/\/www. people. cornell. edu\/pages\/df36\/CONJINTRnew% 20TEX. pdf 46(1997)."},{"key":"e_1_3_2_2_15_1","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Flanagan Adrian","unstructured":"Adrian Flanagan , Were Oyomno , Alexander Grigorievskiy , Kuan\u00a0 E. Tan , Suleiman\u00a0 A. Khan , and Muhammad Ammad-Ud-Din . 2021. Federated Multi-view Matrix Factorization for Personalized Recommendations . In Machine Learning and Knowledge Discovery in Databases . Springer International Publishing , Cham , 324\u2013347. Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan\u00a0E. Tan, Suleiman\u00a0A. Khan, and Muhammad Ammad-Ud-Din. 2021. Federated Multi-view Matrix Factorization for Personalized Recommendations. In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, Cham, 324\u2013347."},{"key":"e_1_3_2_2_16_1","volume-title":"Bayesian data analysis","author":"Gelman Andrew","unstructured":"Andrew Gelman , John\u00a0 B Carlin , Hal\u00a0 S Stern , David\u00a0 B Dunson , Aki Vehtari , and Donald\u00a0 B Rubin . 2013. Bayesian data analysis . CRC press . Andrew Gelman, John\u00a0B Carlin, Hal\u00a0S Stern, David\u00a0B Dunson, Aki Vehtari, and Donald\u00a0B Rubin. 2013. Bayesian data analysis. CRC press."},{"key":"e_1_3_2_2_17_1","volume-title":"International Conference on Machine Learning. PMLR, 100\u2013108","author":"Gopalan Aditya","year":"2014","unstructured":"Aditya Gopalan , Shie Mannor , and Yishay Mansour . 2014 . Thompson sampling for complex online problems . In International Conference on Machine Learning. PMLR, 100\u2013108 . Aditya Gopalan, Shie Mannor, and Yishay Mansour. 2014. Thompson sampling for complex online problems. In International Conference on Machine Learning. PMLR, 100\u2013108."},{"key":"e_1_3_2_2_18_1","unstructured":"Pengchao Han Shiqiang Wang and Kin\u00a0K Leung. 2020. Adaptive gradient sparsification for efficient federated learning: An online learning approach. arXiv preprint arXiv:2001.04756(2020).  Pengchao Han Shiqiang Wang and Kin\u00a0K Leung. 2020. Adaptive gradient sparsification for efficient federated learning: An online learning approach. arXiv preprint arXiv:2001.04756(2020)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"F\u00a0Maxwell Harper and Joseph\u00a0A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5 4(2015) 1\u201319.  F\u00a0Maxwell Harper and Joseph\u00a0A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5 4(2015) 1\u201319.","DOI":"10.1145\/2827872"},{"key":"e_1_3_2_2_20_1","volume-title":"Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. Advances in Neural Information Processing Systems 33","author":"He Chaoyang","year":"2020","unstructured":"Chaoyang He , Murali Annavaram , and Salman Avestimehr . 2020. Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. Advances in Neural Information Processing Systems 33 ( 2020 ). Chaoyang He, Murali Annavaram, and Salman Avestimehr. 2020. Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_2_22_1","volume-title":"Federated distillation and augmentation under non-IID private data. NIPS Wksp. MLPCD","author":"Jeong E","year":"2018","unstructured":"E Jeong , S Oh , H Kim , J Park , M Bennis , and SL Kim . 2018. Federated distillation and augmentation under non-IID private data. NIPS Wksp. MLPCD ( 2018 ). E Jeong, S Oh, H Kim, J Park, M Bennis, and SL Kim. 2018. Federated distillation and augmentation under non-IID private data. NIPS Wksp. MLPCD (2018)."},{"key":"e_1_3_2_2_23_1","unstructured":"Jaya Kawale Hung\u00a0H Bui Branislav Kveton Long Tran-Thanh and Sanjay Chawla. 2015. Efficient thompson sampling for online matrix-factorization recommendation. In Advances in neural information processing systems. 1297\u20131305.  Jaya Kawale Hung\u00a0H Bui Branislav Kveton Long Tran-Thanh and Sanjay Chawla. 2015. Efficient thompson sampling for online matrix-factorization recommendation. In Advances in neural information processing systems. 1297\u20131305."},{"key":"e_1_3_2_2_24_1","volume-title":"International Conference on Learning Representations.","author":"Kingma P","year":"2015","unstructured":"Diederik\u00a0 P Kingma and Jimmy\u00a0Lei Ba . 2015 . Adam: A method for Stochastic Optimization . In International Conference on Learning Representations. Diederik\u00a0P Kingma and Jimmy\u00a0Lei Ba. 2015. Adam: A method for Stochastic Optimization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_25_1","volume-title":"Federated Learning: Strategies for Improving Communication Efficiency. CoRR abs\/1610.05492(2016). arxiv:1610.05492http:\/\/arxiv.org\/abs\/1610.05492","author":"Konecn\u00fd Jakub","year":"2016","unstructured":"Jakub Konecn\u00fd , H.\u00a0 Brendan McMahan , Felix\u00a0 X. Yu , Peter Richt\u00e1rik , Ananda\u00a0Theertha Suresh , and Dave Bacon . 2016 . Federated Learning: Strategies for Improving Communication Efficiency. CoRR abs\/1610.05492(2016). arxiv:1610.05492http:\/\/arxiv.org\/abs\/1610.05492 Jakub Konecn\u00fd, H.\u00a0Brendan McMahan, Felix\u00a0X. Yu, Peter Richt\u00e1rik, Ananda\u00a0Theertha Suresh, and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. CoRR abs\/1610.05492(2016). arxiv:1610.05492http:\/\/arxiv.org\/abs\/1610.05492"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_27_1","unstructured":"Qinbin Li Zeyi Wen and Bingsheng He. 2019. Federated learning systems: Vision hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693(2019).  Qinbin Li Zeyi Wen and Bingsheng He. 2019. Federated learning systems: Vision hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693(2019)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"e_1_3_2_2_29_1","volume-title":"The Twenty-Eighth International Flairs Conference.","author":"Lou\u00ebdec Jonathan","year":"2015","unstructured":"Jonathan Lou\u00ebdec , Max Chevalier , Josiane Mothe , Aur\u00e9lien Garivier , and S\u00e9bastien Gerchinovitz . 2015 . A multiple-play bandit algorithm applied to recommender systems . In The Twenty-Eighth International Flairs Conference. Jonathan Lou\u00ebdec, Max Chevalier, Josiane Mothe, Aur\u00e9lien Garivier, and S\u00e9bastien Gerchinovitz. 2015. A multiple-play bandit algorithm applied to recommender systems. In The Twenty-Eighth International Flairs Conference."},{"key":"e_1_3_2_2_30_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics. 1273\u20131282.  Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics. 1273\u20131282."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403176"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Tao Qi Fangzhao Wu Chuhan Wu Yongfeng Huang and Xing Xie. 2020. Privacy-Preserving News Recommendation Model Training via Federated Learning. arXiv preprint arXiv:2003.09592(2020).  Tao Qi Fangzhao Wu Chuhan Wu Yongfeng Huang and Xing Xie. 2020. Privacy-Preserving News Recommendation Model Training via Federated Learning. arXiv preprint arXiv:2003.09592(2020).","DOI":"10.18653\/v1\/2020.findings-emnlp.128"},{"key":"e_1_3_2_2_33_1","unstructured":"Jiangcheng Qin and Baisong Liu. 2020. A Novel Privacy-Preserved Recommender System Framework based on Federated Learning. arXiv preprint arXiv:2011.05614(2020).  Jiangcheng Qin and Baisong Liu. 2020. A Novel Privacy-Preserved Recommender System Framework based on Federated Learning. arXiv preprint arXiv:2011.05614(2020)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390255"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.3007021"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"key":"e_1_3_2_2_37_1","volume-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler , Simon Wiedemann , Klaus-Robert M\u00fcller , and Wojciech Samek . 2019. Robust and communication-efficient federated learning from non-iid data . IEEE transactions on neural networks and learning systems 31, 9( 2019 ), 3400\u20133413. Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. IEEE transactions on neural networks and learning systems 31, 9(2019), 3400\u20133413."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.874"},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the 21st International Conference on Neural Information Processing Systems. 1577\u20131584","author":"Streeter Matthew","year":"2008","unstructured":"Matthew Streeter and Daniel Golovin . 2008 . An online algorithm for maximizing submodular functions . In Proceedings of the 21st International Conference on Neural Information Processing Systems. 1577\u20131584 . Matthew Streeter and Daniel Golovin. 2008. An online algorithm for maximizing submodular functions. In Proceedings of the 21st International Conference on Neural Information Processing Systems. 1577\u20131584."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3411528"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/25.3-4.285"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.2307\/2371219"},{"key":"e_1_3_2_2_43_1","volume-title":"International Conference on Algorithmic Learning Theory. Springer, 375\u2013389","author":"Uchiya Taishi","year":"2010","unstructured":"Taishi Uchiya , Atsuyoshi Nakamura , and Mineichi Kudo . 2010 . Algorithms for adversarial bandit problems with multiple plays . In International Conference on Algorithmic Learning Theory. Springer, 375\u2013389 . Taishi Uchiya, Atsuyoshi Nakamura, and Mineichi Kudo. 2010. Algorithms for adversarial bandit problems with multiple plays. In International Conference on Algorithmic Learning Theory. Springer, 375\u2013389."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2936565"}],"event":{"name":"RecSys '21: Fifteenth ACM Conference on Recommender Systems","location":"Amsterdam Netherlands","acronym":"RecSys '21","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","SIGecom Special Interest Group on Economics and Computation"]},"container-title":["Fifteenth ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460231.3474257","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3460231.3474257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:17Z","timestamp":1750191137000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460231.3474257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,13]]},"references-count":45,"alternative-id":["10.1145\/3460231.3474257","10.1145\/3460231"],"URL":"https:\/\/doi.org\/10.1145\/3460231.3474257","relation":{},"subject":[],"published":{"date-parts":[[2021,9,13]]}}}