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However, this challenge also presents an opportunity. Designing recommender systems to minimize energy consumption and reduce environmental costs - this is what we define as\n            <jats:italic>Green Recommender Systems<\/jats:italic>\n            - can offer direct benefits not only for sustainability but also for companies and users. By optimizing resource use, these systems can reduce operational costs, improve efficiency, and even enhance user experience through faster recommendations. This paper calls for the recommender systems community to urgently integrate energy efficiency into their designs, considering it alongside traditional performance metrics. Addressing the environmental impact of recommender systems is essential, not just for the planet, but also for the long-term viability and cost-effectiveness of the technology.\n          <\/jats:p>","DOI":"10.1145\/3722449.3722468","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T17:24:20Z","timestamp":1741281860000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Green Recommender Systems: A Call for Attention"],"prefix":"10.1145","volume":"58","author":[{"given":"Joeran","family":"Beel","sequence":"first","affiliation":[{"name":"University of Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"Said","sequence":"additional","affiliation":[{"name":"University of Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Vente","sequence":"additional","affiliation":[{"name":"University of Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Wegmeth","sequence":"additional","affiliation":[{"name":"University of Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2024.143090"},{"key":"e_1_2_1_2_1","volume-title":"International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood) at the 18th ACM Conference on Recommender Systems (ACM RecSys)","author":"Arabzadeh Ardalan","year":"2024","unstructured":"Ardalan Arabzadeh, Tobias Vente, and Joeran Beel. Green recommender systems: Optimizing dataset size for energy-efficient algorithm performance. In International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood) at the 18th ACM Conference on Recommender Systems (ACM RecSys), 2024."},{"key":"e_1_2_1_3_1","volume-title":"International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood) at the 18th ACM Conference on Recommender Systems (ACM RecSys)","author":"Baumgart Moritz","year":"2024","unstructured":"Moritz Baumgart, Lukas Wegmeth, Tobias Vente, and Joeran Beel. e-fold cross-validation for recommender-system evaluation. In International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood) at the 18th ACM Conference on Recommender Systems (ACM RecSys), 2024."},{"key":"e_1_2_1_4_1","volume-title":"Our use of ai-tools for writing research papers","author":"Beel Joeran","year":"2024","unstructured":"Joeran Beel. Our use of ai-tools for writing research papers. In Intelligent Systems Group, Blog, 2024. URL https:\/\/isg.beel.org\/blog\/2024\/08\/19\/our-use-of-ai-tools-for-writing-research-papers\/."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.31219\/osf.io\/exw3j"},{"key":"e_1_2_1_6_1","volume-title":"Strategies of automated machine learning for energy sustainability in green artificial intelligence. Applied Sciences (2076-3417), 14(14)","author":"Castellanos-Nieves Dagoberto","year":"2024","unstructured":"Dagoberto Castellanos-Nieves and Luis Garc\u00eda-Forte. Strategies of automated machine learning for energy sustainability in green artificial intelligence. Applied Sciences (2076-3417), 14(14), 2024."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/app132011583"},{"key":"e_1_2_1_8_1","volume-title":"Seda Polat-Erdeniz, Sebastian Lubos, Merfat El Mansi, Damian Garber, and Viet-Man Le. 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URL https:\/\/arxiv.org\/abs\/2404.01965."},{"key":"e_1_2_1_10_1","volume-title":"International Conference on Artificial Intelligence and Machine Learning Research (CAIMLR), 2024","author":"Mahlich Christopher","year":"2024","unstructured":"Christopher Mahlich, Tobias Vente, and Joeran Beel. From theory to practice: Implementing and evaluating e-fold cross-validation. In International Conference on Artificial Intelligence and Machine Learning Research (CAIMLR), 2024. URL https:\/\/isg.beel.org\/blog\/2024\/09\/16\/e-fold-cross-validation\/."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3565472.3595617"},{"key":"e_1_2_1_12_1","volume-title":"RecSoGood Workshop","author":"Plaza Alejandro","year":"2024","unstructured":"Alejandro Plaza, Juan Gil, and Denis Parra Santander. 14 kg of co2: Analyzing the carbon footprint and performance of session-based recommendation algorithms. 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