Awesome Recommender Systems in Advancing Sustainability

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Recommender systems are revolutionizing sustainability efforts by intelligently guiding users toward eco-friendly choices, from energy-efficient products/models to reduced-waste services. This repo is a curated list of papers about the latest advancements in recommender systems targetting sustainability. This repo is being actively updated, please stay tuned!

Paper: Advancing Sustainability via Recommender Systems: A Survey

Authors: Xin Zhou^1,†, Lei Zhang^1,†, Honglei Zhang², Yixin Zhang³, Xiaoxiong Zhang¹, Jie Zhang¹, and Zhiqi Shen¹

¹Nanyang Technological University, ²Beijing Jiaotong University, ³Shandong University. ^† Equal Contribution

Surveys

0. Recommender systems for smart cities - Quijano-Sánchez, Lara, Iván Cantador, María E. Cortés-Cediel, and Olga Gil. Information systems 92 (2020): 101545.
1. Intelligent tourism recommender systems: A survey - Borràs, Joan, Antonio Moreno, and Aida Valls. "Intelligent tourism recommender systems: A survey." Expert systems with applications 41, no. 16 (2014): 7370-7389.
2. Industrial symbiosis recommender systems - van Capelleveen, Guido Cornelis. Enschede: University of Twente, 2020.
3. A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects - Himeur, Yassine, Abdullah Alsalemi, Ayman Al-Kababji, Faycal Bensaali, Abbes Amira, Christos Sardianos, George Dimitrakopoulos, and Iraklis Varlamis. Information Fusion 72 (2021): 1-21.
4. Systematic review of contextual suggestion and recommendation systems for sustainable e-tourism - Rehman Khan, Haseeb Ur, Chen Kim Lim, Minhaz Farid Ahmed, Kian Lam Tan, and Mazlin Bin Mokhtar. Sustainability 13, no. 15 (2021): 8141.
5. A review of a recommendation filtering system approach based on reliable sustainable opinion mining - Gmach, Imen, Nadia Abaoub, Rubina Khan, Naoufel Mahfoudh, and Amira Kaddour. Technological Sustainability 1, no. 2 (2022): 184-200.
6. Green AI - Schwartz R, Dodge J, Smith N A, et al. Green ai[J]. Communications of the ACM, 2020, 63(12): 54-63.
7. Green Edge AI: A Contemporary Survey - Mao Y, Yu X, Huang K, et al. Green Edge AI: A Contemporary Survey[J]. arXiv preprint arXiv:2312.00333, 2023.
8. Data-centric green artificial intelligence: A survey - Salehi S, Schmeink A. Data-centric green artificial intelligence: A survey[J]. IEEE Transactions on Artificial Intelligence, 2023.
9. Recommender systems for sustainability: overview and research issues - Felfernig A, Wundara M, Tran T N T, et al. Recommender systems for sustainability: overview and research issues[J]. Frontiers in big Data, 2023, 6.
10. Embedding Compression in Recommender Systems: A Survey - Li, Shiwei, Huifeng Guo, Xing Tang, Ruiming Tang, Lu Hou, Ruixuan Li, and Rui Zhang. "Embedding Compression in Recommender Systems: A Survey." ACM Computing Surveys 56, no. 5 (2024): 1-21.

Sustainable Travel Recommendation

0. Recommending eco-friendly route plans - Bothos E, Apostolou D, Mentzas G. Recommending eco-friendly route plans[C]//Proc. of 1st int. workshop on recommendation technologies for lifestyle change. 2012: 12-17.
1. Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques - Makhdomi A A, Gillani I A. Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques[J]. ACM Transactions on Intelligent Systems and Technology, 2023, 15(1): 1-57.
2. Optimal route recommendation for waste carrier vehicles for efficient waste collection: A step forward towards sustainable cities - Ahmad S, Jamil F, Iqbal N, et al. Optimal route recommendation for waste carrier vehicles for efficient waste collection: A step forward towards sustainable cities[J]. IEEE Access, 2020, 8: 77875-77887.
3. Persuasive public-friendly route recommendation with flexible rewards - Sengvong S, Bai Q. Persuasive public-friendly route recommendation with flexible rewards[C]//2017 IEEE International Conference on Agents (ICA). IEEE, 2017: 109-114.
4. Optimal travel route recommendation mechanism based on neural networks and particle swarm optimization for efficient tourism using tourist vehicular data - Malik S, Kim D H. Optimal travel route recommendation mechanism based on neural networks and particle swarm optimization for efficient tourism using tourist vehicular data[J]. Sustainability, 2019, 11(12): 3357.
5. An eco-friendly multimodal route guidance system for urban areas using multi-agent technology - Namoun A, Tufail A, Mehandjiev N, et al. An eco-friendly multimodal route guidance system for urban areas using multi-agent technology[J]. Applied Sciences, 2021, 11(5): 2057.
6. Healthy Track: Healthy Route Recommendation - Nunes A S P. Healthy Track: healthy route recommendation[D]. , 2022.
7. Hybrid route recommendation with taxi and shared bicycles - Zhou H, Zhao Y, Fang J, et al. Hybrid route recommendation with taxi and shared bicycles[J]. Distributed and Parallel Databases, 2020, 38: 563-583.
8. IoT-based route recommendation for an intelligent waste management system - Ghahramani M, Zhou M C, Molter A, et al. IoT-based route recommendation for an intelligent waste management system[J]. IEEE Internet of Things Journal, 2021, 9(14): 11883-11892.
9. An energy-efficient mobile recommender system - Ge Y, Xiong H, Tuzhilin A, et al. An energy-efficient mobile recommender system[C]//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 2010: 899-908.
10. Machine learning-driven apps recommendation for energy optimization in green communication and networking for connected and autonomous vehicles - Xu Y, Lin J, Gao H, et al. Machine learning-driven apps recommendation for energy optimization in green communication and networking for connected and autonomous vehicles[J]. IEEE Transactions on Green Communications and Networking, 2022, 6(3): 1543-1552.
11. Policy recommendations for green travel in medium cities based on signaling data - Yuan L Ü, Haojing G E, Pengpeng J. Policy recommendations for green travel in medium cities based on signaling data[J]. Journal of Tsinghua University (Science and Technology), 2023, 63(11): 1719-1728.
12. Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach - Nilashi M, Ahani A, Esfahani M D, et al. Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach[J]. Journal of Cleaner Production, 2019, 215: 767-783.
13. Recommendation of Sustainable Route Optimization for Travel and Tourism - Kiruthika R, Yiping L, Laohakangvalvit T, et al. Recommendation of Sustainable Route Optimization for Travel and Tourism[C]//International Conference on Human-Computer Interaction. Cham: Springer Nature Switzerland, 2023: 385-396.
14. Route recommendations for idle taxi drivers: Find me the shortest route to a customer! - Nandani Garg and Sayan Ranu. 2018. Route recommendations for idle taxi drivers: Find me the shortest route to a customer!. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18). Association for Computing Machinery, New York, NY, USA, 1425–1434.
15. Beyond shortest paths: Route recommendations for ride-sharing - Chak Fai Yuen, Abhishek Pratap Singh, Sagar Goyal, Sayan Ranu, and Amitabha Bagchi. 2019. Beyond shortest paths: Route recommendations for ride-sharing. In The World Wide Web Conference (WWW ’19). Association for Computing Machinery, New York, NY, USA, 2258–2269.
16. Green Destination Recommender: A Web Application to Encourage Responsible City Trip Recommendations - Ashmi Banerjee, Tunar Mahmudov, and Wolfgang Wörndl. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct 2024), Cagliari, Italy, July 1-4, 2024. Association for Computing Machinery, New York, NY, USA, 2024.
17. Personalized Interest Sustainability Modeling for Sequential POI Recommendation - Zewen Long, Liang Wang, Qiang Liu, and Shu Wu. 2023. Personalized Interest Sustainability Modeling for Sequential POI Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), edited by Ingo Frommholz, Frank Hopfgartner, Mark Lee, Michael Oakes, Mounia Lalmas, Min Zhang, and Rodrygo L. T. Santos, 4145-4149. Birmingham, United Kingdom, October 21-25, 2023. ACM, New York, NY, USA.
18. Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World - Gourab K. Patro, Abhijnan Chakraborty, Ashmi Banerjee, and Niloy Ganguly. 2020. In RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22-26, 2020, edited by Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, and Edleno Silva de Moura, 358-367. ACM, New York, NY, USA.
19. Sustainability Driven Recommender Systems - Pavel Merinov, David Massimo and Francesco Ricci
20. An energy-efficient mobile recommender system - Ge, Yong, et al. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 2010.
21. Recommending eco-friendly route plans - Bothos, Efthimios, Dimitris Apostolou, and Gregoris Mentzas. In Proc. of 1st int. workshop on recommendation technologies for lifestyle change, pp. 12-17, 2012.
22. A socially motivating and environmentally friendly tour recommendation framework for tourist groups - Mehdi Kargar, Zhibin Lin. Expert Systems with Applications, 180 (2021): 115083.
23. An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City - Suanpang, Pannee, et al. Sustainability 15.1 (2023): 455.

Sustainable Food Recommendation

0. Front of Pack Traffic Light Signpost Labelling - Technical Guidance - UK FSA (Food Standards Agency).
1. Population nutrient intake goals for preventing diet-related chronic diseases - WHO (World Health Organization)
2. Nutritional content of supermarket ready meals and recipes by television chefs in the United Kingdom: cross sectional study - Bmj 345 (2012).
3. Estimating the Healthiness of Internet Recipes: A Cross-sectional Study - Frontiers in public health 5 (2017): 244350.
4. Nudging Healthy Choices in Food Search Through Visual Attractiveness - Frontiers in Artificial Intelligence 4 (2021): 621743.
5. A novel healthy and time-aware food recommender system using attributed community detection - Rostami M, Farrahi V, Ahmadian S, et al. Expert Systems with Applications, 2023, 221: 119719.
6. Health-aware food recommendation system with dual attention in heterogeneous graphs - Forouzandeh S, Rostami M, Berahmand K, et al. Computers in Biology and Medicine, 2024, 169: 107882.
7. Towards Health-Aware Fairness in Food Recipe Recommendation - Rostami M, Aliannejadi M, Oussalah M. ACM Conference on Recommender Systems. 2023: 1184-1189.
8. Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation - Song Y, Yang X, Xu C. ACM Transactions on Multimedia Computing, Communications and Applications, 2023, 19(1s): 1-23.
9. SHARE: A Framework for Personalized and Healthy Recipe Recommendations - Zioutos K, Kondylakis H, Stefanidis K. [C]//Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference. CEUR-WS, 2023.
10. Towards Adaptive and Personalised Recommendation for Healthy Food Promotion - Nurbakova D, Bölz F, Serna A, et al. [C]//BehavRec’23, the First International Workshop on Behavior Change and Persuasive Recommender Systems at the 17th ACM Conference on Recommender Systems (RecSys’ 23). 2023.
11. Examining the User Evaluation of Multi-List Recommender Interfaces in the Context of Healthy Recipe Choices - Starke A D, Asotic E, Trattner C, et al. ACM Transactions on Recommender Systems, 2023, 1(4): 1-31.
12. Healthy Personalized Recipe Recommendations for Weekly Meal Planning - [J]. Zioutos K, Kondylakis H, Stefanidis K. Computers, 2023, 13(1): 1.
13. Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement - Ahmadian S, Rostami M, Jalali S M J, et al. [J]. International Journal of Computational Intelligence Systems, 2022, 15(1): 105.
14. A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets - Chavan P, Thoms B, Isaacs J. [J]. 2021.
15. A Recommender System for Healthy and Personalized Recipe Recommendations - Pecune F, Callebert L, Marsella S. [C]//HealthRecSys@ RecSys. 2020: 15-20.
16. An overview of recommender systems in the healthy food domain - Trang Tran T N, Atas M, Felfernig A, et al. [J]. Journal of Intelligent Information Systems, 2018, 50: 501-526.
17. Investigating the Healthiness of Internet-Sourced Recipes - Trattner C, Elsweiler D. Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems[C]//Proceedings of the 26th international conference on world wide web. 2017: 489-498.
18. Sustainable Recipes. A Food Recipe Sourcing and Recommendation System to Minimize Food Miles - Herrera, Juan. arXiv preprint arXiv:2004.07454 (2020).

Sustainable Building Recommendation

0. Short-term building energy model recommendation system: A meta-learning approach - Cui C, Wu T, Hu M, et al. Short-term building energy model recommendation system: A meta-learning approach[J]. Applied energy, 2016, 172: 251-263.
1. Reducing energy waste in households through real-time recommendation - Dahihande J, Jaiswal A, Pagar A A, et al. Reducing energy waste in households through real-time recommendations[C]//Proceedings of the 14th ACM Conference on Recommender Systems. 2020: 545-550.
2. Multi-agent recommendation system for electrical energy optimization and cost saving in smart homes - Jiménez-Bravo D M, Pérez-Marcos J, H. De la Iglesia D, et al. Multi-agent recommendation system for electrical energy optimization and cost saving in smart homes[J]. Energies, 2019, 12(7): 1317.
3. E-commerce for a sustainable future: integrating trust, green supply chain management and online shopping satisfaction - Jalil F, Yang J, Al-Okaily M, et al. E-commerce for a sustainable future: integrating trust, green supply chain management and online shopping satisfaction[J]. Asia Pacific Journal of Marketing and Logistics, 2024.
4. Personalized residential energy usage recommendation system based on load monitoring and collaborative filtering - Luo F, Ranzi G, Kong W, et al. Personalized residential energy usage recommendation system based on load monitoring and collaborative filtering[J]. IEEE transactions on industrial informatics, 2020, 17(2): 1253-1262.
5. Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach - Nilashi M, Ahani A, Esfahani M D, et al. Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach[J]. Journal of Cleaner Production, 2019, 215: 767-783.
6. Multi-agent-based cbr recommender system for intelligent energy management in buildings - Pinto T, Faia R, Navarro-Caceres M, et al. Multi-agent-based CBR recommender system for intelligent energy management in buildings[J]. IEEE Systems Journal, 2018, 13(1): 1084-1095.
7. A recommendation system for energy saving and user engagement in existing buildings - Pruvost H, Calleja-Rodríguez G, Enge-Rosenblatt O, et al. A recommendation system for energy saving and user engagement in existing buildings[J]. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 2022, 176(1): 1-11.
8. Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings - Riabchuk V, Hagel L, Germaine F, et al. Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings[J]. arXiv preprint arXiv:2205.02704, 2022.
9. Smartbuild recsys: A recommendation system based on the smart readiness indicator for energy efficiency in buildings - Siddique M T, Koukaras P, Ioannidis D, et al. SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings[J]. Algorithms, 2023, 16(10): 482.
10. Efficient energy consumption in smart buildings using personalized nilm-based recommender system - Taghvaei F, Safa R. Efficient energy consumption in smart buildings using personalized NILM-based recommender system[J]. Big data and computing visions, 2021, 1(3): 161-169.
11. A deep-reinforcement-learning-based recommender system for occupant-driven energy optimization in commercial buildings - Wei P, Xia S, Chen R, et al. A deep-reinforcement-learning-based recommender system for occupant-driven energy optimization in commercial buildings[J]. IEEE Internet of Things Journal, 2020, 7(7): 6402-6413.
12. Household power usage pattern filtering-based residential electricity plan recommender system - Zhao P, Dong Z Y, Meng K, et al. Household power usage pattern filtering-based residential electricity plan recommender system[J]. Applied Energy, 2021, 298: 117191.

Broader Applications of Sustainable Recommendations

0. Smart sustainability through system satisfaction_tailored preference elicitation for energy-saving recommenders - Knijnenburg, Bart Piet, Martijn Willemsen, and Ron Broeders. (2014).
1. Social networks gamification for sustainability recommendation systems - Silva, Fábio, Cesar Analide, Luís Rosa, Gilberto Felgueiras, and Cedric Pimenta. In Distributed Computing and Artificial Intelligence: 10th International Conference, pp. 307-315. Springer International Publishing, 2013.
2. Keen to advocate green: How green attributes drive product recommendations - Stockheim I, Tevet D, Fenig N. Keen to advocate green: How green attributes drive product recommendations[J]. Journal of Cleaner Production, 2024, 434: 140157.
3. Research and design of green packaging product recommendation system based on multi-algorithm fusion - Liu H, Wang Z. Research and design of green packaging product recommendation system based on multi-algorithm fusion[C]//2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2022: 527-530.
4. nalyzing green recommendation approach to address sustainability issues in textile and apparel supply chain - Gawali V. Analyzing Green Recommendation Approach to Address Sustainability Issues in Textile and Apparel Supply Chain[J]. Asian Journal of Organic & Medicinal Chemistry, 2022.
5. Promoting Green Fashion Consumption Through Digital Nudges in Recommender Systems - Cossatin A G, Mauro N, Ardissono L. Promoting Green Fashion Consumption Through Digital Nudges in Recommender Systems[J]. IEEE Access, 2024.
6. Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendations - Sabina Tomkins, Steven Isley, Ben London, and Lise Getoor. 2018. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018), edited by Sole Pera, Michael D. Ekstrand, Xavier Amatriain, and John O'Donovan, 214-218. Vancouver, BC, Canada, October 2-7, 2018. ACM, New York, NY, USA.
7. Towards Sustainability of Large Language Models for Recommender Systems - Lin, Yuan, Debasish Ghose, David Coates, and Junyong You. Proceedings of 7th FAccTRec Workshop on Responsible Recommendation (FAccTRec ’2024). ACM, New York, NY, USA,

Sustainable Design of Recommender Models

0. A case-based reasoning recommender system for sustainable smart city development - Anthony Jnr, Bokolo. AI & SOCIETY 36.1 (2021): 159-183.
1. Social Networks Gamification for Sustainability Recommendation Systems - Silva, Fábio, et al. Distributed Computing and Artificial Intelligence: 10th International Conference. Springer International Publishing, 2013.
2. An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology - Namoun, Abdallah, et al. Applied Sciences 11.5 (2021): 2057.
3. Bumblebee friendly planting recommendations with citizen science data - Wibowo, Agung Toto, et al. Proceedings of the International Workshop on Recommender Systems for Citizens. 2017.
4. Recommender system architecture for adaptive green marketing - Lee, Ying-Lien, and Fei-Hui Huang. Expert Systems with Applications 38.8 (2011): 9696-9703.
5. The influence of knowledge in the design of a recommender system to facilitate industrial symbiosis markets - van Capelleveen, Guido, et al. Environmental modelling & software 110 (2018): 139-152.
6. Sustainability Driven Recommender Systems - Merinov, Pavel, David Massimo, and Francesco Ricci. (2022).
7. Energy efficient behavior modeling for demand side recommender system in solar microgrid applications using multi-agent reinforcement learning model - Onile, Abiodun E., et al. Sustainable Cities and Society (2023): 104392.
8. Multiobjective recommendation for sustainable productive systems - Pachot, Arnault, et al. MORS workshop held in conjunction with the 15th ACM Conference on Recommender Systems (RecSys), 2021. 2021.
9. Reduce, Reuse, Recycle_Green Information Retrieval Research - Scells, Harrisen, Shengyao Zhuang, and Guido Zuccon. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022.
10. Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business - Guo, Yan, et al. Sustainability 10.1 (2018): 147.
11. Session-Based Recommender System for Sustainable Digital Marketing - Hwangbo, Hyunwoo, and Yangsok Kim. Sustainability 11.12 (2019): 3336.
12. A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks - Lee, Hea In, et al. Sustainability 12.3 (2020): 969.
13. A recommender system for mobility-as-a-service plans selection - Arnaoutaki, Konstantina, et al. Sustainability 13.15 (2021): 8245.
14. Priority Products for Sustainability Information and Recommendation Software: Insights in the Context of the EU’s Action Plan Circular Economy - Wurster, Simone, and Cristina Fróes de Borja Reis. Sustainability 14.19 (2022): 11951.
15. Sustainable Collaborator Recommendation Based on Conference Closure - Wang, Wei, et al. IEEE Transactions on Computational Social Systems 6.2 (2019): 311-322.
16. Towards sustainability-aware recommender systems: analyzing the trade-off between algorithms performance and carbon footprint - Spillo G, De Filippo A, Musto C, et al. Towards sustainability-aware recommender systems: analyzing the trade-off between algorithms performance and carbon footprint[C]//Proceedings of the 17th ACM Conference on Recommender Systems. 2023: 856-862.
17. Benchmarking News Recommendation in the Era of Green AI - Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu. 2024. Benchmarking News Recommendation in the Era of Green AI. In Companion Proceedings of the ACM on Web Conference 2024 (WWW '24), edited by Tat-Seng Chua, Chong-Wah Ngo, Roy Ka-Wei Lee, Ravi Kumar, and Hady W. Lauw. Singapore, May 13-17, 2024. ACM, New York, NY, USA, pp. 971-974.
18. Sustainability-oriented Recommender Systems - Pavel Merinov
19. A survey of resource-efficient llm and multimodal foundation models. Xu, M., Yin, W., Cai, D., Yi, R., Xu, D., Wang, Q., Wu, B., Zhao, Y., Yang, C., Wang, S. and Zhang, Q., 2024. arXiv preprint arXiv:2401.08092.
20. GreenSeq: Automatic Design of Green Networks for Sequential Recommendation Systems - Yankun Ren, Xinxing Yang, Xingyu Lu, Longfei Li, Jun Zhou, Jinjie Gu, and Guannan Zhang. 2023. GreenSeq: Automatic Design of Green Networks for Sequential Recommendation Systems. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), edited by Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete, 3364-3368. Taipei, Taiwan, July 23-27, 2023. ACM, New York, NY, USA.
21. GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System - Xingyu Lu, Zhining Liu, Yanchu Guan, Hongxuan Zhang, Chenyi Zhuang, Wenqi Ma, Yize Tan, Jinjie Gu, and Guannan Zhang. 2023. GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI '23), 19th-25th August 2023, Macao, SAR, China, pp. 6103-6111. ijcai.org.
22. Sustainability-aware collaborative service composition and recommendation based on multi-attribute correlations - Xiahui Liu, Qianwang Deng, Zhangwen Huo, Saibo Liu, Qiang Luo, and Chao Jiang. 2024. Sustainability-aware collaborative service composition and recommendation based on multi-attribute correlations. Expert Syst. Appl., 241, 122642.
23. Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability - Dongmin Hyun, Chanyoung Park, Junsu Cho, and Hwanjo Yu. 2022. Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22), edited by Mohammad Al Hasan and Li Xiong, 812-821. Atlanta, GA, USA, October 17-21, 2022. ACM, New York, NY, USA.
24. From Clicks to Carbon: The Environmental Toll of Recommender Systems - Tobias Vente, Lukas Wegmeth, Alan Said, Joeran Beel. 2024. From Clicks to Carbon: The Environmental Toll of Recommender Systems. RecSys 2024.

Please consider to cite our paper if this repo. helps you, thanks:

@article{zhou2024advancing,
      title={Advancing Sustainability via Recommender Systems: A Survey}, 
      author={Zhou, Xin and Zhang, Lei and Zhang, Honglei and Zhang, Yixin and Zhang, Xiaoxiong and Zhang, Jie and Shen, Zhiqi},
      year={2024},
      journal={arXiv preprint arXiv:2411.07658},
}