GAN inversion and shifting: recommending product modifications to sellers for better user preference
In efforts to better accommodate users, numerous researchers have endeavored to model customer behavior, seeking to comprehend how they interact with diverse items within online platforms. This exploration has given rise to recommendation systems, which utilize customer similarity with other custome...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2553.pdf |
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| author | Satyadwyoom Kumar Abhijith Sharma Apurva Narayan |
| author_facet | Satyadwyoom Kumar Abhijith Sharma Apurva Narayan |
| author_sort | Satyadwyoom Kumar |
| collection | DOAJ |
| description | In efforts to better accommodate users, numerous researchers have endeavored to model customer behavior, seeking to comprehend how they interact with diverse items within online platforms. This exploration has given rise to recommendation systems, which utilize customer similarity with other customers or customer-item interactions to suggest new items based on the existing item catalog. Since these systems primarily focus on enhancing customer experiences, they overlook providing insights to sellers that could help refine the aesthetics of their items and increase their customer coverage. In this study, we go beyond customer recommendations to propose a novel approach: suggesting aesthetic feedback to sellers in the form of refined item images informed by customer-item interactions learned by a recommender system from multiple consumers. These images could serve as guidance for sellers to adapt existing items to meet the dynamic preferences of multiple users simultaneously. To evaluate the effectiveness of our method, we design experiments showcasing how changing the number of consumers and the class of item image used affect the change in preference score. Through these experiments, we found that our methodology outperforms previous approaches by generating distinct, realistic images with user preference higher by 16.7%, thus bridging the gap between customer-centric recommendations and seller-oriented feedback. |
| format | Article |
| id | doaj-art-9d74a949f5fe4d55bcc08ec713523492 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-9d74a949f5fe4d55bcc08ec7135234922025-01-08T15:05:24ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e255310.7717/peerj-cs.2553GAN inversion and shifting: recommending product modifications to sellers for better user preferenceSatyadwyoom Kumar0Abhijith Sharma1Apurva Narayan2Computer Science, Netaji Subhas Institute of Technology, New Delhi, Delhi, IndiaElectrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, CanadaComputer Science, University of Western Ontario, London, Ontario, CanadaIn efforts to better accommodate users, numerous researchers have endeavored to model customer behavior, seeking to comprehend how they interact with diverse items within online platforms. This exploration has given rise to recommendation systems, which utilize customer similarity with other customers or customer-item interactions to suggest new items based on the existing item catalog. Since these systems primarily focus on enhancing customer experiences, they overlook providing insights to sellers that could help refine the aesthetics of their items and increase their customer coverage. In this study, we go beyond customer recommendations to propose a novel approach: suggesting aesthetic feedback to sellers in the form of refined item images informed by customer-item interactions learned by a recommender system from multiple consumers. These images could serve as guidance for sellers to adapt existing items to meet the dynamic preferences of multiple users simultaneously. To evaluate the effectiveness of our method, we design experiments showcasing how changing the number of consumers and the class of item image used affect the change in preference score. Through these experiments, we found that our methodology outperforms previous approaches by generating distinct, realistic images with user preference higher by 16.7%, thus bridging the gap between customer-centric recommendations and seller-oriented feedback.https://peerj.com/articles/cs-2553.pdfRecommendatiomn systemsMachine learning |
| spellingShingle | Satyadwyoom Kumar Abhijith Sharma Apurva Narayan GAN inversion and shifting: recommending product modifications to sellers for better user preference PeerJ Computer Science Recommendatiomn systems Machine learning |
| title | GAN inversion and shifting: recommending product modifications to sellers for better user preference |
| title_full | GAN inversion and shifting: recommending product modifications to sellers for better user preference |
| title_fullStr | GAN inversion and shifting: recommending product modifications to sellers for better user preference |
| title_full_unstemmed | GAN inversion and shifting: recommending product modifications to sellers for better user preference |
| title_short | GAN inversion and shifting: recommending product modifications to sellers for better user preference |
| title_sort | gan inversion and shifting recommending product modifications to sellers for better user preference |
| topic | Recommendatiomn systems Machine learning |
| url | https://peerj.com/articles/cs-2553.pdf |
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