Promoting Green Fashion Consumption in Recommender Systems
Pages 50 - 54
Abstract
The fashion industry is a significant contributor to global carbon emissions, water consumption, and waste generation. My Ph.D. project explores two approaches to promote sustainability in fashion recommender systems. First, I aim to develop algorithms and user interfaces that nudge users towards more sustainable and ethical fashion choices by incorporating relevant data into the recommendation process and item presentation. This involves balancing user preferences with sustainability considerations and exploring effective ways to present this complex information to users. Second, I seek to make the recommendation algorithms themselves more environmentally sustainable by reducing their computational cost without compromising recommendation quality. To achieve these goals, I plan to leverage multimodal item representations, multi-criteria recommendation techniques, and novel algorithmic approaches. I expect to complete my Ph.D. in October, 2025.
References
[1]
Himan Abdollahpouri and Robin Burke. 2022. Multistakeholder Recommender Systems. Springer US, New York, NY, 647–677. https://doi.org/10.1007/978-1-0716-2197-4_17
[2]
Joy Annamma, John F. Sherry Jr, Alladi Venkatesh, Jeff Wang, and Chan Ricky. 2012. Fast Fashion, Sustainability, and the Ethical Appeal of Luxury Brands. Fashion Theory 16, 3 (2012), 273–295. https://doi.org/10.2752/175174112X13340749707123 arXiv:https://doi.org/10.2752/175174112X13340749707123
[3]
Arngeir Berge, Vegard Velle Sjøen, Alain D. Starke, and Christoph Trattner. 2021. Changing Salty Food Preferences with Visual and Textual Explanations in a Search Interface. http://arxiv.org/abs/2108.01427 arXiv:2108.01427 [cs].
[4]
Mustafa Bilgic and Raymond J Mooney. 2005. Explaining Recommendations: Satisfaction vs. Promotion. (2005), 8.
[5]
Said Yacine Boulahia, Abdenour Amamra, Mohamed Ridha Madi, and Said Daikh. 2021. Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition. Machine Vision and Applications 32, 6 (Nov. 2021), 121. https://doi.org/10.1007/s00138-021-01249-8
[6]
Christian Bracher, Sebastian Heinz, and Roland Vollgraf. 2016. Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping. http://arxiv.org/abs/1609.02489 arXiv:1609.02489 [cs].
[7]
Owen Chambers, Robin Cohen, Maura R. Grossman, and Queenie Chen. 2022. Creating a User Model to Support User-specific Explanations of AI Systems. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. ACM, Barcelona Spain, 163–166. https://doi.org/10.1145/3511047.3537678
[8]
Angelo Geninatti Cossatin, Noemi Mauro, and Liliana Ardissono. 2023. Enriching Recommender Systems Results with Data about Sustainability and Ethical Standards of Brands. In 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, Venice, Italy, 238–242. https://doi.org/10.1109/WI-IAT59888.2023.00037
[9]
Angelo Geninatti Cossatin, Noemi Mauro, and Liliana Ardissono. 2024. Promoting Green Fashion Consumption Through Digital Nudges in Recommender Systems. IEEE Access 12 (2024), 6812–6829. https://doi.org/10.1109/ACCESS.2024.3349710
[10]
Maurizio Ferrari Dacrema, Nicolò Felicioni, and Paolo Cremonesi. 2022. Evaluating Recommendations in a User Interface With Multiple Carousels. (2022), 7. https://doi.org/10.3389/fdata.2022.910030
[11]
Yashar Deldjoo, Fatemeh Nazary, Arnau Ramisa, Julian Mcauley, Giovanni Pellegrini, Alejandro Bellogin, and Tommaso Di Noia. 2022. A Review of Modern Fashion Recommender Systems. http://arxiv.org/abs/2202.02757 arXiv:2202.02757 [cs].
[12]
Nima Dokoohaki (Ed.). 2020. Fashion Recommender Systems. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-55218-3
[13]
Ayoub El Majjodi, Alain D. Starke, and Christoph Trattner. 2022. Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (Barcelona, Spain) (UMAP ’22). Association for Computing Machinery, New York, NY, USA, 48–56. https://doi.org/10.1145/3503252.3531312
[14]
David Elsweiler, Hanna Hauptmann, and Christoph Trattner. 2022. Food Recommender Systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer. https://doi.org/10.1007/978-1-0716-2197-4_23
[15]
Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran, Seda Polat-Erdeniz, Sebastian Lubos, Merfat El Mansi, Damian Garber, and Viet-Man Le. 2023. Recommender systems for sustainability: overview and research issues. Frontiers in Big Data 6 (Oct. 2023), 1284511. https://doi.org/10.3389/fdata.2023.1284511
[16]
Matt Fishwick. 2012. A Carbon Footprint for UK Clothing and Opportunities for Savings.
[17]
Suzanna E. Forwood, Amy L. Ahern, Theresa M. Marteau, and Susan A. Jebb. 2015. Offering within-category food swaps to reduce energy density of food purchases: a study using an experimental online supermarket. International Journal of Behavioral Nutrition and Physical Activity 12, 1 (Dec. 2015), 85. https://doi.org/10.1186/s12966-015-0241-1
[18]
Piero Fraternali, Sergio Herrera, Jasminko Novak, Mark Melenhorst, Dimitrios Tzovaras, Stelios Krinidis, Andrea Emilio Rizzoli, Cristina Rottondi, and Francesca Cellina. 2017. enCOMPASS — An integrative approach to behavioural change for energy saving. In 2017 Global Internet of Things Summit (GIoTS). IEEE, Geneva, Switzerland, 1–6. https://doi.org/10.1109/GIOTS.2017.8016256
[19]
Angelo Geninatti Cossatin, Noemi Mauro, Gianmarco Izzi, and Liliana Ardissono. 2023. Synchronized Multi-list User Interfaces for Fashion Catalogs. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. ACM, Limassol Cyprus, 224–228. https://doi.org/10.1145/3563359.3597382
[20]
Sofia Gkika and George Lekakos. 2014. The persuasive role of Explanations in Recommender Systems. International Workshop on Behavior Change Support Systems (2014).
[21]
Good On You. 2024. Good On You - Sustainable and Ethical Fashion Brand Ratings. https://goodonyou.eco/.
[22]
Fabian Grabenhorst, Frank P. Schulte, Stefan Maderwald, and Matthias Brand. 2013. Food labels promote healthy choices by a decision bias in the amygdala. NeuroImage 74 (July 2013), 152–163. https://doi.org/10.1016/j.neuroimage.2013.02.012
[23]
Ulrike Gretzel and Daniel R. Fesenmaier. 2006. Persuasion in Recommender Systems. International Journal of Electronic Commerce 11, 2 (2006), 81–100. https://doi.org/10.2753/JEC1086-4415110204 arXiv:https://doi.org/10.2753/JEC1086-4415110204
[24]
Klaus G. Grunert and Josephine M. Wills. 2007. A review of European research on consumer response to nutrition information on food labels. Journal of Public Health 15, 5 (Oct. 2007), 385–399. https://doi.org/10.1007/s10389-007-0101-9
[25]
Tara Hackett. 2015. A Comparative Life Cycle Assessment of Denim Jeans and a Cotton T-Shirt: The Production of Fast Fashion Essential Items From Cradle to Gate. (2015), 128.
[26]
Dietmar Jannach. 2022. Multi-Objective Recommender Systems: Survey and Challenges. http://arxiv.org/abs/2210.10309 arXiv:2210.10309 [cs].
[27]
Shatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, and Reza Shirvany. 2022. Fashion Recommender Systems. Springer US, New York, NY, 1015–1055. https://doi.org/10.1007/978-1-0716-2197-4_26
[28]
Mathias Jesse and Dietmar Jannach. 2021. Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports 3 (2021), 100052. https://doi.org/10.1016/j.chbr.2020.100052
[29]
Annamma Joy, John F. Sherry, Alladi Venkatesh, Jeff Wang, and Ricky Chan. 2012. Fast Fashion, Sustainability, and the Ethical Appeal of Luxury Brands. Fashion Theory 16, 3 (Sept. 2012), 273–295. https://doi.org/10.2752/175174112X13340749707123 Publisher: Routledge _eprint: https://doi.org/10.2752/175174112X13340749707123.
[30]
Bart Piet Knijnenburg, Martijn Willemsen, and Ron Broeders. 2014. Smart sustainability through system satisfaction: Tailored preference elicitation for energy-saving recommenders. Human Computer Interaction (2014), 16.
[31]
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, and Eugenio Di Sciascio. 2023. Formalizing Multimedia Recommendation through Multimodal Deep Learning. http://arxiv.org/abs/2309.05273 arXiv:2309.05273 [cs].
[32]
Doroteja Mandarić, Anica Hunjet, and Goran Kozina. 2021. Perception of Consumers’ Awareness about Sustainability of Fashion Brands. Journal of Risk and Financial Management 14, 12 (Dec. 2021), 594. https://doi.org/10.3390/jrfm14120594
[33]
Noemi Mauro, Zhongli Filippo Hu, and Liliana Ardissono. 2022. Service-Aware Personalized Item Recommendation. IEEE Access 10 (2022), 26715–26729. https://doi.org/10.1109/ACCESS.2022.3157442
[34]
Noemi Mauro, Hu Zhongli Filippo, and Liliana Ardissono. 2022. Justification of recommender systems results: a service-based approach. User Modeling and User-Adapted Interaction 33, 3 (2022), 643–685. https://doi.org/10.1007/s11257-022-09345-8
[35]
Martijn Millecamp, Sidra Naveed, Katrien Verbert, and Jürgen Ziegler. 2019. To Explain or Not to Explain: the Effects of Personal Characteristics When Explaining Feature-based Recommendations in Different Domains. (2019), 9. https://doi.org/10.1145/3301275.3302313
[36]
Dami Moon, Kiyo Kurisu, and Kiyotaka Tahara. 2023. Which products are bought second-hand and by whom?: Analysis of consumer-preferred acquisition modes by product type. Resources, Conservation and Recycling 190 (2023), 106860. https://doi.org/10.1016/j.resconrec.2022.106860
[37]
Cliona Ni Mhurchu, Helen Eyles, Yannan Jiang, and Tony Blakely. 2018. Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial. Appetite 121 (Feb. 2018), 360–365. https://doi.org/10.1016/j.appet.2017.11.105
[38]
Kirsi Niinimäki, Greg Peters, Helena Dahlbo, Patsy Perry, Timo Rissanen, and Alison Gwilt. 2020. The environmental price of fast fashion. Nature Reviews Earth & Environment 1 (2020), 189–200. https://doi.org/10.1038/s43017-020-0039-9
[39]
SAJN Nikolina. 2019. Environmental impact of textile and clothes industry. (2019), 10.
[40]
Florian Pecune, Lucile Callebert, and Stacy Marsella. 2022. Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies. Frontiers in Robotics and AI 8 (Jan. 2022), 733835. https://doi.org/10.3389/frobt.2021.733835
[41]
Ola Persson and Jennifer B. Hinton. 2023. Second-hand clothing markets and a just circular economy? Exploring the role of business forms and profit. Journal of Cleaner Production 390 (2023), 136139. https://doi.org/10.1016/j.jclepro.2023.136139
[42]
Drude-Katrine Plannthin. 2016. Animal Ethics and Welfare in the Fashion and Lifestyle Industries. In Green Fashion, Subramanian Senthilkannan Muthu and Miguel Angel Gardetti (Eds.). Springer Singapore, Singapore, 49–122. https://doi.org/10.1007/978-981-10-0245-8_3 Series Title: Environmental Footprints and Eco-design of Products and Processes.
[43]
Pearl Pu and Li Chen. 2007. Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 6 (Aug. 2007), 542–556. https://doi.org/10.1016/j.knosys.2007.04.004
[44]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2022. Recommender Systems Handbook. https://link.springer.com/book/10.1007/978-1-0716-2197-4
[45]
Alain Starke, Edis Asotic, and Christoph Trattner. 2021. “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface. In Fifteenth ACM Conference on Recommender Systems. ACM, Amsterdam Netherlands, 124–132. https://doi.org/10.1145/3460231.3474232
[46]
Alain Starke, Ayoub El Majjodi, and Christoph Trattner. 2022. Boosting Health? Examining the Role of Nutrition Labels and Preference Elicitation Methods in Food Recommendation. In Interfaces and Human Decision Making for Recommender Systems 2022(CEUR Workshop Proceedings, Vol. 3222). Rheinisch-Westfaelische Technische Hochschule Aachen, 67–84. 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2022 ; Conference date: 22-09-2022.
[47]
Alain Starke, Martijn Willemsen, and Chris Snijders. 2017. Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System. In Proceedings of the Eleventh ACM Conference on Recommender Systems(RecSys ’17). Association for Computing Machinery, New York, NY, USA, 65–73. https://doi.org/10.1145/3109859.3109902
[48]
Alain D Starke, Justyna Sedkowska, Mihir Chouhan, and Bruce Ferwerda. 2022. Examining Choice Overload across Single-list and Multi-list User Interfaces. (2022), 15.
[49]
Alain D Starke and Christoph Trattner. 2021. Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems. (2021), 3.
[50]
Zalando. 2024. Zalando. https://www.zalando.com.
[51]
Hongyu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, and Zhiqi Shen. 2023. A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions. http://arxiv.org/abs/2302.04473 arXiv:2302.04473 [cs].
Index Terms
- Promoting Green Fashion Consumption in Recommender Systems
Recommendations
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingOver the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
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June 2024
662 pages
ISBN:9798400704666
DOI:10.1145/3631700
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Published: 28 June 2024
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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
July 1 - 4, 2024
Cagliari, Italy
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