{"title":"可解释的时尚兼容性预测:属性增强神经框架","authors":"","doi":"10.1016/j.elerap.2024.101451","DOIUrl":null,"url":null,"abstract":"<div><p>Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.</p></div>","PeriodicalId":50541,"journal":{"name":"Electronic Commerce Research and Applications","volume":null,"pages":null},"PeriodicalIF":5.9000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Explainable fashion compatibility Prediction: An Attribute-Augmented neural framework\",\"authors\":\"\",\"doi\":\"10.1016/j.elerap.2024.101451\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.</p></div>\",\"PeriodicalId\":50541,\"journal\":{\"name\":\"Electronic Commerce Research and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronic Commerce Research and Applications\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1567422324000966\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Commerce Research and Applications","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1567422324000966","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Explainable fashion compatibility Prediction: An Attribute-Augmented neural framework
Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.
期刊介绍:
Electronic Commerce Research and Applications aims to create and disseminate enduring knowledge for the fast-changing e-commerce environment. A major dilemma in e-commerce research is how to achieve a balance between the currency and the life span of knowledge.
Electronic Commerce Research and Applications will contribute to the establishment of a research community to create the knowledge, technology, theory, and applications for the development of electronic commerce. This is targeted at the intersection of technological potential and business aims.