Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems
Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when adapting a recommendation framework to be emp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133307 |
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| _version_ | 1849762915611049984 |
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| author | Aayush Singha Roy Edoardo D'Amico Aonghus Lawlor Neil Hurley |
| author_facet | Aayush Singha Roy Edoardo D'Amico Aonghus Lawlor Neil Hurley |
| author_sort | Aayush Singha Roy |
| collection | DOAJ |
| description | Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when adapting a recommendation framework to be employed in the fashion domain. However, an industry standard real-world recommendation architecture entails numerous phases, including data preparation, establishing and training recommender models, filtering and fulfilling revenue-based user needs. The contributions of the presented work are twofold. We first formalise the multi-stage recommendation pipeline by including the time dimension intrinsically present in the fashion data. We then present a study to incorporate explicit fashion domain characteristics into the presented pipeline. Finally, we conduct comprehensive experimentation on a real-world web-scale fashion dataset released by H\&M, illustrating how including domain knowledge in the multi-stage framework can lead to significantly improvement on the final recommendation performance. |
| format | Article |
| id | doaj-art-41705c0446614f1bbf107bc6031a5dda |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-41705c0446614f1bbf107bc6031a5dda2025-08-20T03:05:35ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13330769613Addressing Fast Changing Fashion Trends in Multi-Stage Recommender SystemsAayush Singha Roy0https://orcid.org/0009-0000-7085-3306Edoardo D'Amico1https://orcid.org/0000-0002-8262-7207Aonghus Lawlor2https://orcid.org/0000-0002-6160-4639Neil Hurley3https://orcid.org/0000-0001-8428-2866University College of Dublin and Insight Centre for Data AnalyticsUniversity College of Dublin and Insight Centre for Data AnalyticsUniversity College of Dublin and Insight Centre for Data AnalyticsUniversity College of Dublin and Insight Centre for Data AnalyticsFashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when adapting a recommendation framework to be employed in the fashion domain. However, an industry standard real-world recommendation architecture entails numerous phases, including data preparation, establishing and training recommender models, filtering and fulfilling revenue-based user needs. The contributions of the presented work are twofold. We first formalise the multi-stage recommendation pipeline by including the time dimension intrinsically present in the fashion data. We then present a study to incorporate explicit fashion domain characteristics into the presented pipeline. Finally, we conduct comprehensive experimentation on a real-world web-scale fashion dataset released by H\&M, illustrating how including domain knowledge in the multi-stage framework can lead to significantly improvement on the final recommendation performance.https://journals.flvc.org/FLAIRS/article/view/133307multi-stage recommender systemsfashion recommendationlearning to rank |
| spellingShingle | Aayush Singha Roy Edoardo D'Amico Aonghus Lawlor Neil Hurley Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems Proceedings of the International Florida Artificial Intelligence Research Society Conference multi-stage recommender systems fashion recommendation learning to rank |
| title | Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems |
| title_full | Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems |
| title_fullStr | Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems |
| title_full_unstemmed | Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems |
| title_short | Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems |
| title_sort | addressing fast changing fashion trends in multi stage recommender systems |
| topic | multi-stage recommender systems fashion recommendation learning to rank |
| url | https://journals.flvc.org/FLAIRS/article/view/133307 |
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