{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:13:28Z","timestamp":1792790008415,"version":"4.5.0"},"reference-count":127,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2026,12,31]]},"abstract":"<jats:p>\n                    As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems community hardly understands, addresses, and evaluates the environmental impact of their work. In this study, we examine the environmental impact of recommender systems research by reproducing typical experimental pipelines. Based on our results, we provide guidelines for researchers and practitioners on how to minimize the environmental footprint of their work and implement green recommender systems\u2014recommender systems designed to minimize their energy consumption and carbon footprint. Our analysis covers 79 papers from the 2013 and 2023 ACM RecSys conferences, comparing traditional \u201cgood old-fashioned AI\u201d models with modern deep learning models. We designed and reproduced representative experimental pipelines for both years, measuring energy consumption using a hardware energy meter and converting it into CO\n                    <jats:sub>2<\/jats:sub>\n                    equivalents. Our results show that papers utilizing deep learning models emit approximately 42 times more CO\n                    <jats:sub>2<\/jats:sub>\n                    equivalents than papers using traditional models. On average, a single deep learning-based paper generates 2,909 kilograms of CO\n                    <jats:sub>2<\/jats:sub>\n                    equivalents\u2014more than the carbon emissions of a person flying from New York City to Melbourne or the amount of CO\n                    <jats:sub>2<\/jats:sub>\n                    sequestered by one tree over 260 years. This work underscores the urgent need for the recommender systems and wider machine learning communities to adopt green AI principles, balancing algorithmic advancements and environmental responsibility to build a sustainable future with AI-powered personalization.\n                  <\/jats:p>","DOI":"10.1145\/3768626","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T11:05:20Z","timestamp":1758798320000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8848-9434","authenticated-orcid":false,"given":"Lukas","family":"Wegmeth","sequence":"first","affiliation":[{"name":"Intelligent Systems Group, University of Siegen","place":["Siegen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8881-2379","authenticated-orcid":false,"given":"Tobias","family":"Vente","sequence":"additional","affiliation":[{"name":"Intelligent Systems Group, University of Siegen","place":["Siegen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2929-0529","authenticated-orcid":false,"given":"Alan","family":"Said","sequence":"additional","affiliation":[{"name":"University of Gothenburg","place":["Goteborg, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4537-5573","authenticated-orcid":false,"given":"Joeran","family":"Beel","sequence":"additional","affiliation":[{"name":"Intelligent Systems Group, University of Siegen","place":["Siegen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463245"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/2014GL059234"},{"issue":"1","key":"e_1_3_3_4_2","first-page":"6","article-title":"Apache mahout: Machine learning on distributed dataflow systems","volume":"21","author":"Anil Robin","year":"2020","unstructured":"Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, and \u00d6zg\u00fcr Yilmazel. 2020. Apache mahout: Machine learning on distributed dataflow systems. Journal of Machine Learning Research 21, 1, Article 127 (2020), 6 pages.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_5_2","unstructured":"Lasse F. Wolff Anthony Benjamin Kanding and Raghavendra Selvan. 2020. Carbontracker: Tracking and predicting the carbon footprint of training deep learning models."},{"key":"e_1_3_3_6_2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/978-3-031-87654-7_7","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Arabzadeh Ardalan","year":"2025","unstructured":"Ardalan Arabzadeh, Tobias Vente, and Joeran Beel. 2025. Green recommender systems: Optimizing dataset size for energy-efficient algorithm performance. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 73\u201382."},{"key":"e_1_3_3_7_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-3-031-87654-7_3","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Banerjee Ashmi","year":"2025","unstructured":"Ashmi Banerjee, Adithi Satish, and Wolfgang W\u00f6rndl. 2025. Enhancing tourism recommender systems for sustainable city trips using retrieval-augmented generation. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 19\u201334."},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.2307\/633502"},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/978-3-031-87654-7_9","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Baumgart Moritz","year":"2025","unstructured":"Moritz Baumgart, Lukas Wegmeth, Tobias Vente, and Joeran Beel. 2025. e-Fold Cross-Validation for recommender-system evaluation. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 90\u201397."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","unstructured":"Joeran Beel Alan Said Tobias Vente and Lukas Wegmeth. 2024. Green Recommender Systems \u2013 A Call for Attention. DOI:10.31219\/osf.io\/5ru2g","DOI":"10.31219\/osf.io\/5ru2g"},{"key":"e_1_3_3_11_2","unstructured":"Filippo Betello Antonio Purificato Vittoria Vineis Gabriele Tolomei and Fabrizio Silvestri. 2025. One search fits All: Pareto-Optimal eco-friendly model selection. Retrieved from https:\/\/arxiv.org\/abs\/2505.01468"},{"key":"e_1_3_3_12_2","first-page":"S118\u2013S128","volume-title":"Proceedings of the Doklady Mathematics","author":"Budennyy Semen Andreevich","year":"2022","unstructured":"Semen Andreevich Budennyy, Vladimir Dmitrievich Lazarev, Nikita Nikolaevich Zakharenko, Aleksei N. Korovin, O. A. Plosskaya, Denis Valer\u2019evich Dimitrov, V. S. Akhripkin, I. V. Pavlov, Ivan Valer\u2019evich Oseledets, Ivan Segundovich Barsola, et\u00a0al. 2022. Eco2ai: Carbon emissions tracking of machine learning models as the first step towards sustainable ai. In Proceedings of the Doklady Mathematics. Springer, S118\u2013S128."},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41612-019-0086-4"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1145\/2043932.2044016","volume-title":"Proceedings of the 5th ACM Conference on Recommender Systems (RecSys\u201911)","author":"Cantador Ivan","year":"2011","unstructured":"Ivan Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011). In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys\u201911). Association for Computing Machinery, New York, NY, USA, 387\u2013388. DOI:10.1145\/2043932.2044016"},{"key":"e_1_3_3_15_2","article-title":"Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting","volume":"7","author":"Cao Yunfeng","year":"2017","unstructured":"Yunfeng Cao, Shunlin Liang, Xiaona Chen, Tao He, Dongdong Wang, and Xiao Cheng. 2017. Enhanced wintertime greenhouse effect reinforcing Arctic amplification and initial sea-ice melting. Scientific Reports 7, 1 (2017), 8462. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:26478317","journal-title":"Scientific Reports"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v103.i15"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2020.07.013"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8454327"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1145\/3604915.3608771","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems (RecSys\u201923)","author":"Chen Huiyuan","year":"2023","unstructured":"Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das, and Hao Yang. 2023. Adversarial collaborative filtering for free. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys\u201923). Association for Computing Machinery, New York, NY, USA, 245\u2013255. DOI:10.1145\/3604915.3608771"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/11\/4\/048002"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","unstructured":"Benoit Courty Victor Schmidt Goyal-Kamal MarionCoutarel Boris Feld J\u00e9r\u00e9my Lecourt LiamConnell SabAmine Inimaz Supatomic Mathilde L\u00e9val Luis Blanche Alexis Cruveiller Ouminasara Franklin Zhao Aditya Joshi Alexis Bogroff Amine Saboni Hugues de Lavoreille Niko Laskaris Edoardo Abati Douglas Blank Ziyao Wang Armin Catovic Alencon Micha\u0142 St\u0229ch\u0142y Christian Bauer Lucas-Otavio JPW and MinervaBooks. 2024. mlco2\/codecarbon: v2.4.1. Zenodo. DOI:10.5281\/zenodo.11171501","DOI":"10.5281\/zenodo.11171501"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.2760\/4002897"},{"key":"e_1_3_3_24_2","volume-title":"Conversion Factors 2020 Methodology","author":"Strategy Department for Business, Energy and Industrial","year":"2020","unstructured":"Department for Business, Energy and Industrial Strategy. 2020. Conversion Factors 2020 Methodology. Technical Report. Government of the United Kingdom. Retrieved from https:\/\/assets.publishing.service.gov.uk\/media\/5f119b673a6f405c059f6060\/conversion-factors-2020-methodology.pdf"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412778"},{"key":"e_1_3_3_26_2","article-title":"Carbon intensity of electricity generation \u2013 Ember and Energy Institute","year":"2024","unstructured":"Ember and Energy Institute. 2024. Carbon intensity of electricity generation \u2013 Ember and Energy Institute. Retrieved October 10, 2025 from https:\/\/ourworldindata.org\/grapher\/carbon-intensity-electricity","journal-title":"Retrieved October 10, 2025 from"},{"key":"e_1_3_3_27_2","first-page":"S33\u2013S7","article-title":"The Environmental history of computing","volume":"59","author":"Ensmenger Nathan L.","year":"2018","unstructured":"Nathan L. Ensmenger. 2018. The Environmental history of computing. Technology and Culture 59, 4 (2018), S33\u2013S7. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:58621216","journal-title":"Technology and Culture"},{"key":"e_1_3_3_28_2","unstructured":"Ahmad Faiz Sotaro Kaneda Ruhan Wang Rita Osi Prateek Sharma Fan Chen and Lei Jiang. 2024. LLMCarbon: Modeling the end-to-end carbon footprint of large language models. Retrieved from https:\/\/arxiv.org\/abs\/2309.14393"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3609498"},{"key":"e_1_3_3_30_2","series-title":"Proceedings of Machine Learning Research","first-page":"1437","volume-title":"Proceedings of the 35th International Conference on Machine Learning.","volume":"80","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner, Aaron Klein, and Frank Hutter. 2018. BOHB: Robust and efficient hyperparameter optimization at scale. In Proceedings of the 35th International Conference on Machine Learning.Jennifer Dy and Andreas Krause (Eds.), Proceedings of Machine Learning Research, Vol. 80, PMLR, 1437\u20131446. Retrieved from https:\/\/proceedings.mlr.press\/v80\/falkner18a.html"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2023.1284511"},{"key":"e_1_3_3_32_2","volume-title":"Proceedings of the 5th ACM International Conference on Recommender Systems (RecSys 2011).","author":"Gantner Zeno","year":"2011","unstructured":"Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. MyMediaLite: A free recommender system library. In Proceedings of the 5th ACM International Conference on Recommender Systems (RecSys 2011)."},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2019.07.007"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00076"},{"issue":"12","key":"e_1_3_3_35_2","doi-asserted-by":"crossref","first-page":"e81648","DOI":"10.1371\/journal.pone.0081648","article-title":"Assessing \u201cdangerous climate change\u201d: Required reduction of carbon emissions to protect young people, future generations and nature","volume":"8","author":"Hansen James","year":"2013","unstructured":"James Hansen, Pushker Kharecha, Makiko Sato, Valerie Masson-Delmotte, Frank Ackerman, David J Beerling, Paul J Hearty, Ove Hoegh-Guldberg, Shi-Ling Hsu, Camille Parmesan, et\u00a0al. 2013. Assessing \u201cdangerous climate change\u201d: Required reduction of carbon emissions to protect young people, future generations and nature. PloS One 8, 12 (2013), e81648.","journal-title":"PloS One"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_3_3_37_2","article-title":"Climate change and mental health: Risks, impacts and priority actions","volume":"12","author":"Hayes Katie","year":"2018","unstructured":"Katie Hayes, Grant Blashki, John Wiseman, Susie E. L. Burke, and Lennart Reifels. 2018. Climate change and mental health: Risks, impacts and priority actions. International Journal of Mental Health Systems 12, 1 (2018), 28. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:44184035","journal-title":"International Journal of Mental Health Systems"},{"issue":"1","key":"e_1_3_3_38_2","first-page":"43","article-title":"Towards the systematic reporting of the energy and carbon footprints of machine learning","volume":"21","author":"Henderson Peter","year":"2020","unstructured":"Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau. 2020. Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research 21, 1, Article 248 (2020), 43 pages.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.02.002"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aba8232"},{"key":"e_1_3_3_41_2","unstructured":"International Energy Agency (IEA). 2023. CO2 Emissions in 2022. Retrieved October 10 2025 from https:\/\/www.iea.org\/reports\/co2-emissions-in-2022. Licence: CC BY 4.0."},{"key":"e_1_3_3_42_2","unstructured":"International Energy Agency. 2024. Germany - Energy Mix. Retrieved from https:\/\/www.iea.org\/countries\/germany\/energy-mix. Accessed: 2024-12-06."},{"key":"e_1_3_3_43_2","unstructured":"International Energy Agency. 2024. Sweden - Coal. Retrieved from https:\/\/www.iea.org\/countries\/sweden\/coal. Accessed: 2024-12-06."},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","unstructured":"Dietmar Jannach Alan Said Marko Tkalcic and Markus Zanker. 2025. Recommender systems for Good (RS4Good): Survey of use cases and a call to action for research that matters. ACM Trans. Recomm. Syst. (June 2025). DOI:10.1145\/3746648","DOI":"10.1145\/3746648"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","unstructured":"Mathilde Jay Vladimir Ostapenco Laurent Lefevre Denis Trystram Anne-C\u00e9cile Orgerie and Benjamin Fichel. 2023. An experimental comparison of software-based power meters: Focus on CPU and GPU. In 2023 IEEE\/ACM 23rd International Symposium on Cluster Cloud and Internet Computing (CCGrid). 106\u2013118. DOI:10.1109\/CCGrid57682.2023.00020","DOI":"10.1109\/CCGrid57682.2023.00020"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-022-01377-7"},{"issue":"3","key":"e_1_3_3_47_2","first-page":"294","article-title":"Investigation of carbon footprint effect of renewable power plants regarding energy production: A case study of a city in Turkey","volume":"72","author":"Kerem Alper","year":"2022","unstructured":"Alper Kerem. 2022. Investigation of carbon footprint effect of renewable power plants regarding energy production: A case study of a city in Turkey. Journal of the Air and Waste Management Association 72, 3 (2022), 294\u2013307.","journal-title":"Journal of the Air and Waste Management Association"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","unstructured":"Susanne K\u00f6hler and Massimo Pizzol. 2019. Life cycle assessment of bitcoin mining. Environmental Science & Technology 53 23 (December 2019) 13598\u201313606. DOI:10.1021\/acs.est.9b05687","DOI":"10.1021\/acs.est.9b05687"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.9734\/JSRR\/2017\/39630"},{"issue":"6","key":"e_1_3_3_50_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9734\/JSRR\/2017\/39630","article-title":"Greenhouse effect: Greenhouse gases and their impact on global warming","volume":"17","author":"Kweku Darkwah Williams","year":"2018","unstructured":"Darkwah Williams Kweku, Odum Bismark, Addae Maxwell, Koomson Ato Desmond, Kwakye Benjamin Danso, Ewurabena Asante Oti-Mensah, Asenso Theophilus Quachie, and Buanya Beryl Adormaa. 2018. Greenhouse effect: Greenhouse gases and their impact on global warming. Journal of Scientific Research and Reports 17, 6 (2018), 1\u20139.","journal-title":"Journal of Scientific Research and Reports"},{"key":"e_1_3_3_51_2","unstructured":"Alexandre Lacoste Alexandra Luccioni Victor Schmidt and Thomas Dandres. 2019. Quantifying the carbon emissions of machine learning. Retrieved from https:\/\/arxiv.org\/abs\/1910.09700"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-023-00461-y"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202100707"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.63317\/4pmv5q3r3yc5"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","unstructured":"Pengfei Li Jianyi Yang Mohammad A. Islam and Shaolei Ren. 2025. Making AI Less \u201cThirsty.\u201d Commun. ACM 68 7 (June 2025) 54\u201361. DOI:10.1145\/3724499","DOI":"10.1145\/3724499"},{"key":"e_1_3_3_56_2","series-title":"Proceedings of Machine Learning Research","first-page":"5958","volume-title":"Proceedings of the 37th International Conference on Machine Learning.","volume":"119","author":"Li Zhuohan","year":"2020","unstructured":"Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer, Dan Klein, and Joey Gonzalez. 2020. Train big, then compress: Rethinking model size for efficient training and inference of transformers. In Proceedings of the 37th International Conference on Machine Learning.Hal Daum\u00e9 III and Aarti Singh (Eds.), Proceedings of Machine Learning Research, Vol. 119, PMLR, 5958\u20135968. Retrieved from https:\/\/proceedings.mlr.press\/v119\/li20m.html"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","unstructured":"Jianghao Lin Xinyi Dai Yunjia Xi Weiwen Liu Bo Chen Hao Zhang Yong Liu Chuhan Wu Xiangyang Li Chenxu Zhu Huifeng Guo Yong Yu Ruiming Tang and Weinan Zhang. 2025. How can recommender systems benefit from large language models: A survey. ACM Trans. Inf. Syst. 43 2 (January 2025). DOI:10.1145\/3678004","DOI":"10.1145\/3678004"},{"key":"e_1_3_3_58_2","unstructured":"Alexandra Sasha Luccioni and Alex Hernandez-Garcia. 2023. Counting Carbon: A survey of factors influencing the emissions of machine learning. arXiv preprint arXiv:2302.08476 (2023)."},{"key":"e_1_3_3_59_2","first-page":"253:1\u2013253:15","article-title":"Estimating the carbon footprint of BLOOM, a 176B parameter language model","volume":"24","author":"Luccioni Alexandra Sasha","year":"2022","unstructured":"Alexandra Sasha Luccioni, Sylvain Viguier, and Anne-Laure Ligozat. 2022. Estimating the carbon footprint of BLOOM, a 176B parameter language model. Journal of Machine Learning Research 24, 1 (2022), 253:1\u2013253:15. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:253265387","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/ac2966"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608791"},{"key":"e_1_3_3_62_2","volume-title":"Global Warming of 1.5\u00b0c : An IPCC Special Report on Impacts of Global Warming of 1.5\u00b0c Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Contex of Strengthening the Global Response to the Thereat of Blimate Change, Sustainable Development, and Efforts to Eradicate Poverty","author":"Masson-Delmotte Val\u00e9rie","year":"2022","unstructured":"Val\u00e9rie Masson-Delmotte and Intergovernmental Panel on Climate Change. 2022. Global Warming of 1.5\u00b0c : An IPCC Special Report on Impacts of Global Warming of 1.5\u00b0c Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Contex of Strengthening the Global Response to the Thereat of Blimate Change, Sustainable Development, and Efforts to Eradicate Poverty. Cambridge University Press. Retrieved from https:\/\/cir.nii.ac.jp\/crid\/1130574323982454028"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781009157896.022"},{"issue":"15","key":"e_1_3_3_64_2","doi-asserted-by":"crossref","first-page":"5718","DOI":"10.3390\/en16155718","article-title":"A review for green energy machine learning and AI services","volume":"16","author":"Mehta Yukta","year":"2023","unstructured":"Yukta Mehta, Rui Xu, Benjamin Lim, Jane Wu, and Jerry Gao. 2023. A review for green energy machine learning and AI services. Energies 16, 15 (2023), 5718.","journal-title":"Energies"},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3591109"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","unstructured":"Lien Michiels Robin Verachtert and Bart Goethals. 2022. RecPack: An(other) experimentation toolkit for Top-N recommendation using implicit feedback data. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys\u201922) Association for Computing Machinery Seattle WA USA 648\u2013651. DOI:10.1145\/3523227.3551472","DOI":"10.1145\/3523227.3551472"},{"key":"e_1_3_3_67_2","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781. Retrieved from https:\/\/arxiv.org\/abs\/1301.3781"},{"key":"e_1_3_3_68_2","unstructured":"T. Minka J. M. Winn J. P. Guiver Y. Zaykov D. Fabian and J. Bronskill. 2018. \/Infer.NET 0.3."},{"issue":"1","key":"e_1_3_3_69_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1029\/RG027i001p00115","article-title":"The \u201cgreenhouse\u201d effect and climate change","volume":"27","author":"Mitchell John F. B.","year":"1989","unstructured":"John F. B. Mitchell. 1989. The \u201cgreenhouse\u201d effect and climate change. Reviews of Geophysics 27, 1 (1989), 115\u2013139.","journal-title":"Reviews of Geophysics"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2880197"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1018"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10784-020-09500-9"},{"key":"e_1_3_3_73_2","unstructured":"Intergovernmental Panel on Climate Change (IPCC). 2018. Global Warming of 1.5\u00b0C: Summary for Policymakers. Retrieved from https:\/\/www.ipcc.ch\/sr15\/. Accessed: 2024-11-21."},{"key":"e_1_3_3_74_2","article-title":"Determining the anthropogenic greenhouse gas contribution to the observed intensification of extreme precipitation","volume":"47","author":"Paik Seungmok","year":"2020","unstructured":"Seungmok Paik, Seung\u2010Ki Min, Xuebin Zhang, Markus G. Donat, Andrew David King, and Qiaohong Sun. 2020. Determining the anthropogenic greenhouse gas contribution to the observed intensification of extreme precipitation. Geophysical Research Letters 47, 12 (2020), e2019GL086875. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:219458077","journal-title":"Geophysical Research Letters"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","unstructured":"Jaeryang Park Hojin Jang Sinhyeok Choi Ryeong Jung and Sokhee P. Jung. 2022. Current and prospects of waste heat utilization and cooling technology in data centers. Journal of Korean Society of Environmental Engineers 44 11 (November 2022) 493\u2013503. DOI:10.4491\/ksee.2022.44.11.493","DOI":"10.4491\/ksee.2022.44.11.493"},{"key":"e_1_3_3_76_2","unstructured":"David Patterson Joseph Gonzalez Quoc Le Chen Liang Lluis-Miquel Munguia Daniel Rothchild David So Maud Texier and Jeff Dean. 2021. Carbon emissions and large neural network training. Retrieved from https:\/\/arxiv.org\/abs\/2104.10350"},{"key":"e_1_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3304332"},{"key":"e_1_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aogh.2014.10.001"},{"key":"e_1_3_3_79_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-3-031-87654-7_12","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Plaza Alejandro","year":"2025","unstructured":"Alejandro Plaza, Juan Carlos Gil, and Denis Parra. 2025. 14 Kg of CO2: Analyzing the carbon footprint and performance of session-based recommendation algorithm. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 123\u2013134."},{"key":"e_1_3_3_80_2","doi-asserted-by":"crossref","DOI":"10.2172\/1910045","volume-title":"A Comprehensive Economic Coal Transition in South Asia","author":"Pressburger Leeya D.","year":"2022","unstructured":"Leeya D. Pressburger, Meredydd Evans, Sha Yu, Yiyun Cui, Abhishek Somani, and Gokul C. Iyer. 2022. A Comprehensive Economic Coal Transition in South Asia. Technical Report. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)."},{"key":"e_1_3_3_81_2","volume-title":"RecSys\u201913: Proceedings of the 7th ACM Conference on Recommender Systems","author":"Pu Pearl","year":"2013","unstructured":"Pearl Pu and George Karypis (Eds.). 2013. RecSys\u201913: Proceedings of the 7th ACM Conference on Recommender Systems (Hong Kong, China). Association for Computing Machinery, New York, NY, USA."},{"key":"e_1_3_3_82_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/978-3-031-87654-7_11","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Purificato Antonio","year":"2025","unstructured":"Antonio Purificato and Fabrizio Silvestri. 2025. Eco-aware graph neural networks for sustainable recommendations. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 111\u2013122."},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-018-0282-y"},{"key":"e_1_3_3_84_2","article-title":"Recommender systems for social good: The role of accountability and sustainability.","author":"Said Alan","year":"2024","unstructured":"Alan Said. 2024. Recommender systems for social good: The role of accountability and sustainability. In Proceeding of the 2024 Workshop on Recommender Systems for Social Good. Springer.","journal-title":"In Proceeding of the 2024 Workshop on Recommender Systems for Social Good"},{"key":"e_1_3_3_85_2","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1109\/TAI.2023.3315272","article-title":"Data-centric green artificial intelligence: A survey","volume":"5","author":"Salehi Shirin","year":"2024","unstructured":"Shirin Salehi and Anke Schmeink. 2024. Data-centric green artificial intelligence: A survey. IEEE Transactions on Artificial Intelligence 5 (2024), 1973\u20131989. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:261933683","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"e_1_3_3_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSC61953.2024.10603302"},{"key":"e_1_3_3_87_2","volume-title":"Proceedings of the Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the 5th Assessment Report of the Intergovernmental Panel on Climate Change","author":"Schl\u00f6mer S.","year":"2014","unstructured":"S. Schl\u00f6mer, T. Bruckner, L. Fulton, E. Hertwich, A. McKinnon, D. Perczyk, J. Roy, R. Schaeffer, R. Sims, P. Smith, and R. Wiser. 2014. Annex III: Technology-specific cost and performance parameters. In Proceedings of the Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the 5th Assessment Report of the Intergovernmental Panel on Climate Change. O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schl\u00f6mer, C. von Stechow, T. Zwickel, and J.C. Minx (Eds.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA."},{"key":"e_1_3_3_88_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0701976104"},{"key":"e_1_3_3_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3609488"},{"key":"e_1_3_3_90_2","doi-asserted-by":"crossref","first-page":"109304","DOI":"10.1016\/j.rser.2019.109304","article-title":"A review on carbon emission reduction in industries and planning emission limits","volume":"114","author":"Sinha Rakesh Kumar","year":"2019","unstructured":"Rakesh Kumar Sinha and Nitin Dutt Chaturvedi. 2019. A review on carbon emission reduction in industries and planning emission limits. Renewable and Sustainable Energy Reviews 114, October 2019 (2019), 109304.","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"e_1_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.erss.2022.102493"},{"key":"e_1_3_3_92_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608840"},{"key":"e_1_3_3_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688160"},{"key":"e_1_3_3_94_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/978-3-031-87654-7_10","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Spillo Giuseppe","year":"2025","unstructured":"Giuseppe Spillo, Alberto Gaetano Valerio, Felice Franchini, Allegra De Filippo, Cataldo Musto, Michela Milano, and Giovanni Semeraro. 2025. RecSys CarbonAtor: Predicting carbon footprint of recommendation system models. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 98\u2013110."},{"key":"e_1_3_3_95_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1355"},{"key":"e_1_3_3_96_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_3_97_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-024-10313-0"},{"key":"e_1_3_3_98_2","doi-asserted-by":"publisher","DOI":"10.1257\/jep.23.2.29"},{"key":"e_1_3_3_99_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.envres.2019.04.012","article-title":"Preventing and mitigating health risks of climate change.","volume":"174","author":"Tong Shilu","year":"2019","unstructured":"Shilu Tong and Kristie L. Ebi. 2019. Preventing and mitigating health risks of climate change. Environmental Research 174 (2019), 9\u201313. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:133607161","journal-title":"Environmental Research"},{"key":"e_1_3_3_100_2","article-title":"Human-driven greenhouse gas and aerosol emissions cause distinct regional impacts on extreme fire weather","volume":"12","author":"Touma Danielle","year":"2021","unstructured":"Danielle Touma, Samantha Stevenson, Flavio Lehner, and Sloan Coats. 2021. Human-driven greenhouse gas and aerosol emissions cause distinct regional impacts on extreme fire weather. Nature Communications 12, 1 (2021), 212. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:231585934","journal-title":"Nature Communications"},{"key":"e_1_3_3_101_2","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-021-00043-6"},{"key":"e_1_3_3_102_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez \u0139ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917). Curran Associates Inc. Long Beach California USA 6000\u20136010."},{"key":"e_1_3_3_103_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3610656"},{"key":"e_1_3_3_104_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688074"},{"key":"e_1_3_3_105_2","article-title":"A systematic review of green AI","volume":"13","author":"Verdecchia Roberto","year":"2023","unstructured":"Roberto Verdecchia, June Sallou, and Lu\u00eds Cruz. 2023. A systematic review of green AI. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13, 4 (2023), e1507. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:256274885","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"e_1_3_3_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401131"},{"key":"e_1_3_3_107_2","volume-title":"Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2022","author":"Wegmeth Lukas","year":"2022","unstructured":"Lukas Wegmeth and Joeran Beel. 2022. CaMeLS: Cooperative meta-learning service for recommender systems. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2022. CEUR-WS. Retrieved from https:\/\/ceur-ws.org\/Vol-3228\/paper2.pdf"},{"key":"e_1_3_3_108_2","series-title":"CEUR Workshop Proceedings","volume-title":"Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, September 19, 2023.","volume":"3476","author":"Wegmeth Lukas","year":"2023","unstructured":"Lukas Wegmeth, Tobias Vente, Lennart Purucker, and Joeran Beel. 2023. The effect of random seeds for data splitting on recommendation accuracy. In Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, September 19, 2023.Alan Said, Eva Zangerle, and Christine Bauer 0001 (Eds.), CEUR Workshop Proceedings, Vol. 3476, CEUR-WS.org. Retrieved from https:\/\/ceur-ws.org\/Vol-3476\/paper4.pdf"},{"key":"e_1_3_3_109_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/978-3-031-87654-7_8","volume-title":"Proceedings of the Recommender Systems for Sustainability and Social Good","author":"Wegmeth Lukas","year":"2025","unstructured":"Lukas Wegmeth, Tobias Vente, Alan Said, and Joeran Beel. 2025. EMERS: Energy meter for recommender systems. In Proceedings of the Recommender Systems for Sustainability and Social Good. Ludovico Boratto, Allegra De Filippo, Elisabeth Lex, and Francesco Ricci (Eds.), Springer Nature Switzerland, Cham, 83\u201389."},{"key":"e_1_3_3_110_2","doi-asserted-by":"crossref","first-page":"116530","DOI":"10.1016\/j.jenvman.2022.116530","article-title":"The environmental impact of cryptocurrencies using proof of work and proof of stake consensus algorithms: A systematic review.","volume":"326","author":"Wendl Moritz","year":"2022","unstructured":"Moritz Wendl, My Hanh Doan, and Remmer Sassen. 2022. The environmental impact of cryptocurrencies using proof of work and proof of stake consensus algorithms: A systematic review. Journal of Environmental Management 326, Part A (2022), 116530. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:253476551","journal-title":"Journal of Environmental Management"},{"key":"e_1_3_3_111_2","unstructured":"World Resources Institute. 2022. Climate Watch Historical GHG Emissions. Retrieved October 10 2025 from https:\/\/www.climatewatchdata.org\/ghg-emissions"},{"key":"e_1_3_3_112_2","unstructured":"Carole-Jean Wu Ramya Raghavendra Udit Gupta Bilge Acun Newsha Ardalani Kiwan Maeng Gloria Chang Fiona Aga Jinshi Huang Charles Bai Michael Gschwind Anurag Gupta Myle Ott Anastasia Melnikov Salvatore Candido David Brooks Geeta Chauhan Benjamin Lee Hsien-Hsin Lee Bugra Akyildiz Maximilian Balandat Joe Spisak Ravi Jain Mike Rabbat and Kim Hazelwood. 2022. Sustainable AI: Environmental implications challenges and opportunities. In Proceedings of Machine Learning and Systems. 795\u2013813. Retrieved from https:\/\/proceedings.mlsys.org\/paper_files\/paper\/2022\/file\/462211f67c7d858f663355eff93b745e-Paper.pdf"},{"key":"e_1_3_3_113_2","doi-asserted-by":"publisher","unstructured":"Likang Wu Zhi Zheng Zhaopeng Qiu Hao Wang Hongchao Gu Tingjia Shen Chuan Qin Chen Zhu Hengshu Zhu Qi Liu Hui Xiong and Enhong Chen. 2024. A survey on large language models for recommendation. World Wide Web 27 5 (August 2024). DOI:10.1007\/s11280-024-01291-2","DOI":"10.1007\/s11280-024-01291-2"},{"key":"e_1_3_3_114_2","first-page":"2837","volume-title":"Proceedings of the SIGIR","author":"Xu Lanling","year":"2023","unstructured":"Lanling Xu, Zhen Tian, Gaowei Zhang, Junjie Zhang, Lei Wang, Bowen Zheng, Yifan Li, Jiakai Tang, Zeyu Zhang, Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Xu Chen, and Ji-Rong Wen. 2023. Towards a more user-friendly and easy-to-use benchmark library for recommender systems. In Proceedings of the SIGIR. ACM, 2837\u20132847."},{"key":"e_1_3_3_115_2","first-page":"491","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems (RecSys\u201923)","author":"Xu Zitao","year":"2023","unstructured":"Zitao Xu, Weike Pan, and Zhong Ming. 2023. A multi-view graph contrastive learning framework for cross-domain sequential recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys\u201923). Association for Computing Machinery, New York, NY, USA, 491\u2013501. 10.1145\/3604915.3608785"},{"key":"e_1_3_3_116_2","doi-asserted-by":"publisher","unstructured":"Tan Yigitcanlar Rashid Mehmood and Juan M. Corchado. 2021. Green artificial intelligence: Towards an efficient sustainable and equitable technology for smart cities and futures. 13. DOI:10.3390\/su13168952","DOI":"10.3390\/su13168952"},{"key":"e_1_3_3_117_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/B978-0-12-819657-1.00001-3","volume-title":"Proceedings of the Advances in Carbon Capture","author":"Yoro Kelvin O.","year":"2020","unstructured":"Kelvin O. Yoro and Michael O. Daramola. 2020. CO2 emission sources, greenhouse gases, and the global warming effect. In Proceedings of the Advances in Carbon Capture. Elsevier, 3\u201328."},{"key":"e_1_3_3_118_2","volume-title":"Proceedings of the IEEE International Conference of Data Mining","author":"Yu Hsiang-Fu","year":"2012","unstructured":"Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, and Inderjit S. Dhillon. 2012. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In Proceedings of the IEEE International Conference of Data Mining."},{"key":"e_1_3_3_119_2","first-page":"44","volume-title":"Proceedings of the 15th ACM Conference on Recommender Systems (RecSys\u201921)","author":"Yue Zhenrui","year":"2021","unstructured":"Zhenrui Yue, Zhankui He, Huimin Zeng, and Julian McAuley. 2021. Black-box attacks on sequential recommenders via data-free model extraction. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys\u201921). Association for Computing Machinery, New York, NY, USA, 44\u201354. DOI:10.1145\/3460231.3474275"},{"key":"e_1_3_3_120_2","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546770"},{"key":"e_1_3_3_121_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915"},{"key":"e_1_3_3_122_2","doi-asserted-by":"publisher","DOI":"10.1145\/3285029"},{"key":"e_1_3_3_123_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2021.02.153"},{"key":"e_1_3_3_124_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-demo.22"},{"key":"e_1_3_3_125_2","unstructured":"Xin Zhou Lei Zhang Honglei Zhang Yixin Zhang Xiaoxiong Zhang Jie Zhang and Zhiqi Shen. 2024. Advancing sustainability via recommender systems: A survey. arXiv preprint arXiv:2411.07658 (2024)."},{"key":"e_1_3_3_126_2","unstructured":"You Zhou Xiujing Lin Xiang Zhang Maolin Wang Gangwei Jiang Huakang Lu Yupeng Wu Kai Zhang Zhe Yang Kehang Wang Yongduo Sui Fengwei Jia Zuoli Tang Yao Zhao Hongxuan Zhang Tiannuo Yang Weibo Chen Yunong Mao Yi Li De Bao Yu Li Hongrui Liao Ting Liu Jingwen Liu Jinchi Guo Xiangyu Zhao Ying WEI Hong Qian Qi Liu Xiang Wang Wai Kin Chan Chenliang Li Yusen Li Shiyu Yang Jining Yan Chao Mou Shuai Han Wuxia Jin Guannan Zhang and Xiaodong Zeng. 2023. On the Opportunities of Green Computing: A Survey. Retrieved from https:\/\/arxiv.org\/abs\/2311.00447"},{"key":"e_1_3_3_127_2","first-page":"2912","volume-title":"Proceedings of the SIGIR\u201922: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11-15, 2022","author":"Zhu Jieming","year":"2022","unstructured":"Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, and Rui Zhang. 2022. BARS: Towards open benchmarking for recommender systems. In Proceedings of the SIGIR\u201922: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11-15, 2022. Enrique Amig\u00f3, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.), ACM, 2912\u20132923. DOI:10.1145\/3477495.3531723"},{"key":"e_1_3_3_128_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608802"}],"container-title":["ACM Transactions on Recommender Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3768626","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T11:33:51Z","timestamp":1785324831000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":127,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,12,31]]}},"alternative-id":["10.1145\/3768626"],"URL":"https:\/\/doi.org\/10.1145\/3768626","relation":{},"ISSN":["2770-6699"],"issn-type":[{"value":"2770-6699","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,29]]},"assertion":[{"value":"2025-03-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}