可解释时尚兼容性建模方法综述

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Karolina Selwon, Julian Szyma?ski
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引用次数: 0

摘要

本文回顾了时尚兼容性推荐领域所使用的方法。我们根据可重复性、可解释性和新颖性等方面来选择方法,然后按时间顺序和主题来组织这些方法。我们介绍了与时尚兼容性推荐任务相关的公开可用数据集的一般特征。最后,我们分析了数据集的代表性偏差、基于时尚的算法的可持续性以及可解释模型评估。本文介绍了实际问题的解释、方法和已发布的数据集,这些数据集可作为进一步研究的灵感来源。本文提出的调查结构对时尚推荐领域的知识进行了梳理,对于那些想从头开始学习该主题、扩展知识面或寻找新的研究领域的人来说都将大有裨益。此外,本文所包含的信息还有助于开发有效且符合道德规范的时尚推荐系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review of explainable fashion compatibility modeling methods

The paper reviews methods used in the fashion compatibility recommendation domain. We select methods based on reproducibility, explainability, and novelty aspects and then organize them chronologically and thematically. We presented general characteristics of publicly available datasets that are related to the fashion compatibility recommendation task. Finally, we analyzed the representation bias of datasets, fashion-based algorithms’ sustainability, and explainable model assessment. The paper describes practical problem explanations, methodologies, and published datasets that may serve as an inspiration for further research. The proposed structure of the survey organizes knowledge in the fashion recommendation domain and will be beneficial for those who want to learn the topic from scratch, expand their knowledge, or find a new field for research. Furthermore, the information included in this paper could contribute to developing an effective and ethical fashion-based recommendation system.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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