{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:51Z","timestamp":1761176151247,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>The pursuit of personalized outfit recommendation, tailored to individual user preferences, has emerged as a central focus in research. Despite the propositions of various outfit recommendation methods, the modeling of fashion styles within outfits, a pivotal criterion for user selection, has been largely overlooked. Fashion styles are intricately embedded in multi-modal information, such as images and text. Unfortunately, current outfit recommendation methods predominantly focus on a single modality or employ simplistic fusion techniques, thus failing to capture the intricate interplay between different fashion data modalities, resulting in an inability to model fashion styles effectively. In this work, we devise a novel Personalized Outfit Recommender with Style-Guided Multi-Modal Feature Fusion scheme, denoted as POSM, aiming to maximize the utilization of multi-modal features in outfit recommendation. In particular, this scheme consists of three key components: modality-aware outfit modeling, feature separation network, and style mapping component. Extensive experiments conducted on the benchmark datasets validate the superiority of POSM over state-of-the-art methods.<\/jats:p>","DOI":"10.3233\/faia250922","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:05Z","timestamp":1761126365000},"source":"Crossref","is-referenced-by-count":0,"title":["POSM: A Personalized Outfit Recommendation System with Style-Guided Multi-Modal Feature Fusion"],"prefix":"10.3233","author":[{"given":"Hengyu","family":"You","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0036-9436","authenticated-orcid":false,"given":"Jian","family":"Cao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Yang","family":"Gu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Qiqi","family":"Cai","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250922","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:16Z","timestamp":1761126376000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250922","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}