Director of Research (if dissertation) or Advisor (if thesis)
Forsyth, David A.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
embeddings
fashion
fashion recommendation
computer vision
Abstract
In this work, we present a novel methodology to recommend items that are compatible with a given item of clothing. Compatibility is a hard notion to capture because of its diversity and subjectivity. We propose an embedding based approach to solve this problem, and perform recommendation based on product-closeness to the given clothing item. We perform this by first decomposing the notion of product-closeness into two inter-related notions of product similarity and product compatibility. Then, we incorporate product type into our embedding mechanism, and learn different embedding networks for different product types. We evaluate our proposed strategy extensively, and demonstrate that it performs better than the baseline, and is an effective method for performing few-shot transfer to compatibility prediction tasks.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
