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.
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Index Terms
- Promoting Green Fashion Consumption in Recommender Systems
Recommendations
Acquiring User Information Needs for Recommender Systems
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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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