7.3. PCW-DC 83
7.3 PCW-DC
is section details the proposed PCW-DC. We first formulate the research problem and then
detail the two key components of the scoring model: user modeling and garment modeling,
based on which we can perform the PCW creation.
7.3.1 PROBLEM FORMULATION
In this work, to be cost-friendly, we focus on creating a PCW based on the user’s original
wardrobe (i.e., the set of historical purchased fashion items). Let I
u
D fi
u
ck
j c D1; : : : ; C I k D
1; : : : ; N
c
g be the original wardrobe of the user u, comprising a set of fashion items from C cat-
egories (e.g., the top, bottom and outer), where N
c
denotes the total number of items belonging
to the category c. In addition, we have a set of items I D fi
n
g
N
nD1
, and each item i
n
is associated
with a visual image and a textual description. Our task is to generate a new personalized capsule
wardrobe
e
I
u
for the user u based on I
u
and I that provides the user both compatible and suit-
able outfits. In a sense, we should get rid of inappropriate items from I
u
and add proper items
from I to maximize the user-garment and garment-garment compatibilities of the wardrobe.
Essentially, we aim to propose a comprehensive wardrobe compatibility scoring model
S./, based on which we can perform the PCW creation. In particular, we define S./ as follows:
S
I
D ˛U
I
j‚
U
C .1 ˛/G
I
j‚
G
; (7.1)
where I
represents a candidate wardrobe. U and G denote the compatibility modeling from
the user-garment and garment-garment perspectives, respectively. ˛ is a trade-off parameter to
balance the evaluation score of each component. ‚
U
and ‚
G
refer to the to-be-learned model
parameters of the user modeling and garment modeling, respectively.
User Preference Modeling
Intuitively, it is reasonable to argue that different individuals may prefer different item appear-
ances and categories. For example, some people may prefer the white top instead of a black one,
while others prefer the skirt rather than the short. In fact, user preference modeling in fashion
domain has been studied by recent work [34], whereby two latent spaces are introduced to mea-
sure the user’s overall preference and visual preference for a given item, respectively. However,
this method overlooks the value of the item’s textual context in the user preference modeling.
In fact, the textual description, including the item title and category metadata, can summarize
the key semantic features of items, like the style, material and category, and hence deliver im-
portant cues of the user preferences. erefore, in this work, to comprehensively model the user
preferences, we formulate x
p
ui
as follows:
x
p
ui
D
T
u
i
C
T
u
W
p
Œ
f
i
; t
i
C ˇ
p
; (7.2)
where
u
2 R
K
and
i
2 R
K
are latent factors of the user u and the item i, respectively.
u
2 R
D
is the latent content factor of the user u. Œf
i
; t
i
refers to the concatenation of item visual feature