Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/218200
Title: COMPUTATIONAL FASHION PREFERENCE MODELING  
Authors: MA YUNSHAN
Keywords: Fashion Analysis, Multimedia Analysis, Recommender System, Time Series Forecasting, Graph Neural Network, Knowledge Extraction
Issue Date: 15-Nov-2021
Citation: MA YUNSHAN (2021-11-15). COMPUTATIONAL FASHION PREFERENCE MODELING  . ScholarBank@NUS Repository.
Abstract: Human’s fashion preference expresses each individual’s personalities by adopting or composing a set of preferable fashion elements. Properly understanding and capturing human’s fashion preference provide important value to the fashion industry. However, traditional research on fashion preference is mainly carried out by fashion experts, which is highly professional and inefficient as it requires a lot of manually crafted data. With the fast development of the Internet, Data Science, and Artificial Intelligence in recent decades, novel computational solutions are surfacing to revamp this traditional research area, and we term this strand of research as Computational Fashion Preference Modeling. In this thesis, we progressively conduct four studies in computational fashion preference modeling: fashion knowledge from social media, fashion trend forecasting based on social media, personalized sequential fashion recommendation, and personalized outfit recommendation.
URI: https://scholarbank.nus.edu.sg/handle/10635/218200
Appears in Collections:Ph.D Theses (Open)

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