Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system

Congying Guan, Sheng-feng Qin, Wessie Ling, Yang Long

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Smart apparel recommendation system is a kind of machine learning system ap-plied to clothes online shopping. The performance quality of the system is greatly dependent on apparel data quality as well as the system learning ability. This pa-per proposes (1) to enhance knowledge-based apparel data based on fashion communication theories and (2) to use deep learning driven methods for apparel data training. The acquisition of new apparel data is supported by apparel visual communication and sign theories. A two-step data training model is proposed. The first step is to predict apparel ATTRIBUTEs from the image data through a multi-task CNN model. The second step is to learn apparel MEANINGs from predicted attributes through SVM and LKF classifiers. The testing results show that the prediction rate of eleven predefined MEANING classes can reach the range from 80.1% to 93.5%. The two-step apparel learning model is applicable for novel recommendation system developments.
Original languageEnglish
Title of host publicationTrends and Advances in Information Systems and Technologies
Subtitle of host publicationWorldCIST'18 2018
PublisherSpringer
Pages31-40
Number of pages10
Volume747
ISBN (Electronic)978-3-319-77700-9
ISBN (Print)9783319776996
DOIs
Publication statusPublished - 27 Mar 2018

Publication series

NameAdvances in Intelligent Systems and Computing (AISC)
PublisherSpringer
Volume747

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