Paper Info
Title
Prototype-guided Attribute-wise Interpretable Scheme for Clothing Matching
Abstract
Recently, as an essential part of people's daily life, clothing matching has gained increasing research attention. Most existing efforts focus on the numerical compatibility modeling between fashion items with advanced neural networks, and hence suffer from the poor interpretation, which makes them less applicable in real world applications. In fact, people prefer to know not only whether the given fashion items are compatible, but also the reasonable interpretations as well as suggestions regarding how to make the incompatible outfit harmonious. Considering that the research line of the comprehensively interpretable clothing matching is largely untapped, in this work, we propose a prototype-guided attribute-wise interpretable compatibility modeling (PAICM) scheme, which seamlessly integrates the latent compatible/incompatible prototype learning and compatibility modeling with the Bayesian personalized ranking (BPR) framework. In particular, the latent attribute interaction prototypes, learned by the non-negative matrix factorization (NMF), are treated as templates to interpret the discordant attribute and suggest the alternative item for each fashion item pair. Extensive experiments on the real-world dataset have demonstrated the effectiveness of our scheme.
Year
DOI
Venue
2019
10.1145/3331184.3331245
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
Field
DocType
fashion analysis, interpretable compatibility modeling, non-negative matrix factorization
Data mining,Computer science,Clothing,Non-negative matrix factorization,Artificial neural network
Conference
ISBN
Citations 
PageRank 
978-1-4503-6172-9
8
0.45
References 
Authors
0
5
Name
Order
Citations
PageRank
Xianjing Han1472.40
Xuemeng Song234822.62
Jianhua Yin31227.34
Ying-long Wang4123.21
Liqiang Nie52975131.85