skip to main content
10.1145/3640457.3688160acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
short-paper

Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances

Published: 08 October 2024 Publication History

Abstract

This work investigates the path toward green recommender systems by examining the impact of data reduction on both model performance and carbon footprint. In the pursuit of developing energy-efficient recommender systems, we investigated whether and how reducing the training data impacts the performances of several representative recommendation models. In order to obtain a fair comparison, all the models were run based on the implementations available in a popular recommendation library, i.e., RecBole, and used the same experimental settings. Results indicate that: (a) data reduction can be a promising strategy to make recommender systems more sustainable, at the cost of a lower accuracy; (b) training recommender systems with less data makes the suggestions more diverse and less biased. Overall, this study contributes to the ongoing discourse on the development of recommendation models that meet the principles of SDGs, laying the groundwork for the adoption of more sustainable practices in the field.

References

[1]
Janneth Chicaiza and Priscila Valdiviezo-Diaz. 2021. A comprehensive survey of knowledge graph-based recommender systems: Technologies, development, and contributions. Information 12, 6 (2021), 232.
[2]
Allegra De Filippo, Andrea Borghesi, Andrea Boscarino, and Michela Milano. 2022. HADA: An automated tool for hardware dimensioning of AI applications. Knowledge-Based Systems 251 (2022), 109199.
[3]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20). Association for Computing Machinery, New York, NY, USA, 639–648. https://doi.org/10.1145/3397271.3401063
[4]
Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010. Recommender systems: an introduction. Cambridge University Press.
[5]
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2020. A Re-visit of the Popularity Baseline in Recommender Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20). Association for Computing Machinery, New York, NY, USA, 1749–1752. https://doi.org/10.1145/3397271.3401233
[6]
Barrie Kersbergen, Olivier Sprangers, and Sebastian Schelter. 2022. Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD ’22). Association for Computing Machinery, New York, NY, USA, 150–159. https://doi.org/10.1145/3514221.3517901
[7]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.
[8]
Loïc Lannelongue, Jason Grealey, and Michael Inouye. 2021. Green algorithms: quantifying the carbon footprint of computation. Advanced science 8, 12 (2021), 2100707.
[9]
Yang Li and Xuewei Chao. 2021. Toward sustainability: trade-off between data quality and quantity in crop pest recognition. Frontiers in plant science 12 (2021), 811241.
[10]
Alexandra Sasha Luccioni, Sylvain Viguier, and Anne-Laure Ligozat. 2023. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. Journal of Machine Learning Research 24, 253 (2023), 1–15. http://jmlr.org/papers/v24/23-0069.html
[11]
Divya Pandey, Madhoolika Agrawal, and Jai Shanker Pandey. 2011. Carbon footprint: current methods of estimation. Environmental monitoring and assessment 178 (2011), 135–160.
[12]
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. arXiv preprint arXiv:2104.10350 (2021).
[13]
Ben Purvis, Yong Mao, and Darren Robinson. 2019. Three pillars of sustainability: in search of conceptual origins. Sustainability science 14 (2019), 681–695.
[14]
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni. 2020. Green ai. Commun. ACM 63, 12 (2020), 54–63.
[15]
Giuseppe Spillo, Allegra De Filippo, Cataldo Musto, Michela Milano, and Giovanni Semeraro. 2023. Towards sustainability-aware recommender systems: analyzing the trade-off between algorithms performance and carbon footprint. In Proceedings of the 17th ACM Conference on Recommender Systems. 856–862.
[16]
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243 (2019).
[17]
Muhammad Habib ur Rehman, Victor Chang, Aisha Batool, and Teh Ying Wah. 2016. Big data reduction framework for value creation in sustainable enterprises. International journal of information management 36, 6 (2016), 917–928.
[18]
Aimee Van Wynsberghe. 2021. Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics 1, 3 (2021), 213–218.
[19]
Robin Verachtert, Lien Michiels, and Bart Goethals. 2022. Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training Window. In Perspectives@ RecSys.
[20]
Roberto Verdecchia, June Sallou, and Luís Cruz. 2023. A systematic review of Green AI. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2023), e1507.
[21]
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 417–426. https://doi.org/10.1145/3269206.3271739
[22]
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 968–977. https://doi.org/10.1145/3292500.3330836
[23]
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In The world wide web conference. 3307–3313.
[24]
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge Graph Convolutional Networks for Recommender Systems. In The World Wide Web Conference (San Francisco, CA, USA) (WWW ’19). Association for Computing Machinery, New York, NY, USA, 3307–3313. https://doi.org/10.1145/3308558.3313417
[25]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR’19). Association for Computing Machinery, New York, NY, USA, 165–174. https://doi.org/10.1145/3331184.3331267
[26]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20). Association for Computing Machinery, New York, NY, USA, 1001–1010. https://doi.org/10.1145/3397271.3401137
[27]
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga, Jinshi Huang, Charles Bai, 2022. Sustainable ai: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems 4 (2022), 795–813.
[28]
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32, 1 (2020), 4–24.
[29]
Hong-Jian Xue, Xin-Yu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (Melbourne, Australia) (IJCAI’17). AAAI Press, 3203–3209.
[30]
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative Knowledge Base Embedding for Recommender Systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 353–362. https://doi.org/10.1145/2939672.2939673
[31]
Yongfeng Zhang, Qingyao Ai, Xu Chen, and Pengfei Wang. 2018. Learning over knowledge-base embeddings for recommendation. arXiv preprint arXiv:1803.06540 (2018).
[32]
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, and Ji-Rong Wen. 2021. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. In CIKM. ACM, 4653–4664.
[33]
Yong Zheng and David Xuejun Wang. 2022. A survey of recommender systems with multi-objective optimization. Neurocomputing 474 (2022), 141–153.

Index Terms

  1. Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 October 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Carbon Footprint
    2. Data Reduction
    3. GreenAI
    4. Recommender Systems

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Conference

    Acceptance Rates

    Overall Acceptance Rate 254 of 1,295 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 137
      Total Downloads
    • Downloads (Last 12 months)137
    • Downloads (Last 6 weeks)20
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media