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Title: Interpretable Fashion Matching with Rich Attributes
Authors: Xun Yang 
Xiangnan He 
Xiang Wang 
Yunshan Ma 
Fuli Feng 
Meng Wang
Tat-Seng Chua 
Keywords: Clothing matching
Fashion compatibility learning
Multimedia recommendation
Issue Date: 21-Jul-2019
Publisher: Association for Computing Machinery, Inc
Citation: Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat-Seng Chua (2019-07-21). Interpretable Fashion Matching with Rich Attributes. SIGIR 2019 : 775-784. ScholarBank@NUS Repository.
Abstract: Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g., off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored. This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a corpus of matched pairs of items, we not only can predict the compatibility score of unseen pairs, but also learn the interpretable patterns that lead to a good match, e.g., white T-shirt matches with black trouser. We propose a new solution named Attribute-based Interpretable Compatibility (AIC) method, which consists of three modules: 1) a tree-based module that extracts decision rules on matching prediction; 2) an embedding module that learns vector representation for a rule by accounting for the attribute semantics; and 3) a joint modeling module that unifies the visual embedding and rule embedding to predict the matching score. To justify our proposal, we contribute a new Lookastic dataset with fashion attributes available. Extensive experiments show that AIC not only outperforms several state-of-the-art methods, but also provides good interpretability on matching decisions. © 2019 Association for Computing Machinery.
Source Title: SIGIR 2019
ISBN: 9781450361729
DOI: 10.1145/3331184.3331242
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