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Table 1: List of adopted clothing attributes and related labels.
Attribute Labels
Gender Male, Female
Item Bottom, Top, Dress
Occasion Sport, Casual, Smart
Style Blouse, Bodycon, Chinos, Hoodie, Jacket, Jeans, Joggies, Jumper, Leggings, Midi, PoloShirt,
Shorts, Shirt, Skater, Skirt, Smock, Sweatshirt, Tie, Top, Trousers, T-shirt, Tux, Twill, Wrap
Fit Muscle, Oversized, Petite, Skinny, Slim
Keyword Bardot, Cable, Check, Chunky, Cold Shoulder, Crop, Drape, Floral, Funnel Neck, Leopard,
Off Shoulder, Oversized, Padded, Pattern, Pencil, Ribbed, Stripe, Tapered, Teddy, Tie, Vneck
Brand Adidas, Asos, Ax Paris, Barbor, Bershka, Boo Hoo, Boss, Daisy Street, Diesel, Farah, Fred Perry,
French Connection, G-Star, Hollister, Homme, JDY, Lacoste, Lee Luke, Lee Malone, Lee Rider,
Levis, Milk, Miss Guided, Miss Selfridge, Monki, Napapijri, New Look, Nike, Nudie, Only,
Pier One, Pretty Little Thing, Puma, Ralph Lauren, Replay, River Island, Stradivarius, Ted Baker,
Tommy, Vero, Vila, Warehouse
Colour Black, Beige, Black, Blue, Brown, Burgundy, Green, Grey, Nude, Orange, Pink, Red, Stone
White, Yellow
Sleeve Sleeveless, Short, Long, 3/4 Length
Length Short, Mini, Midi, Long
Material Cotton, Denim, Leather, Polyamide, Polyester, Satin, Viscose, Wool
clothes the personal stylist likes and not necessary the
ones the user likes. On the other hand, these services
are not expandable at the rate that technologies based
on Artificial Intelligence (AI) can (Luce, 2019).
Indeed, AI techniques can offer a scalable solu-
tion to a wide number of customers, while provid-
ing each of them with a personalized experience and
bespoke advice (Luce, 2019). Hence, since the last
decade (Chen et al., 2013), AI systems have started to
be developed in the retail industry to find trends and
patterns among data, especially for intelligent fashion
analysis (Saponaro et al., 2018) such as clothing mod-
eling (Kim and Cho, 1999), clothing recognition (Seo
and Shin, 2018), clothing parsing (Yamaguchi et al.,
2012), clothing retrieval (Rubio et al., 2017), clothing
pairing (Stan and Mocanu, 2019), or clothing sugges-
tion (Liu et al., 2014).
In particular, clothing recommendation, i.e. cloth-
ing pairing and/or clothing suggestion, can be per-
formed by applying machine learning methods such
as Neural Networks (NN) (Takagi et al., 2017), (Jara-
dat et al., 2018) or Support Vector Machines (SVM)
(Chen et al., 2013), (Liu et al., 2014), (Quinn and
Olszewska, 2019) as well as Self-Organizing Map
(SOM) (Qian and Dong, 2010), (Hao and Hao, 2019)
or association rules (Wakita et al., 2015). How-
ever, the resulting current fashion expert systems are
mainly focused at making automated decisions on ex-
isting clothes within a customer’s wardrobe, based
on criteria such as a specified occasion (Liu et al.,
2014), season (Hao and Hao, 2019), scenery (Jo et al.,
2019), geo-location (Abe et al., 2018), garment colour
(Tu and Dong, 2010) or brand (Wakita et al., 2015).
Moreover, these recommenders rely on large amount
of data that are not always available or could infringe
on data privacy rights (Luce, 2019).
Actually, AI stylists face a number of challenges
that makes fashion understanding complicated, such
as its high level of variability (e.g. through time, sea-
sons, climates, region, culture, etc.) (Takagi et al.,
2017) and subjectivity (e.g. because of age, ethnicity,
places visited, personal interests, mood, etc.) (Priyad-
harsun et al., 2018). A major task lies thus in the
appropriate choice of attributes which describe the
properties of the clothing (Zhao et al., 2017). In-
deed, attributes are used both to learn a user’s personal
style and to make recommendations about what outfit
this customer should wear. It is worth noting that at-
tributes in fashion could be visual (Inoue et al., 2017),
i.e. directly extracted from fashion images by means
of computer-vision methods (Olszewska, 2019), or
semantic ((Valle et al., 2018) with garments being of-
ten described by their materials, colors, patterns, fit,
and cut (Al-Halah et al., 2017).
In this paper, we propose an AI-personal-stylist
web application that embeds a Naive Bayesian Net-
work (NBN) in order to determine suitable, new
clothing suggestions in an automatic way, being given
user’s previous selections that are labeled with se-
mantic features and taking into account customer’s
age and gender, which are provided by the user him-
self/herself at the registration stage of the application.
The adopted machine learning method, i.e. NBN,
is thus a supervised approach which requires only a
small amount of data (voluntarily provided by the
user) to train the system and which can intrinsically
cope with missing data. Ultimately, our approach al-
lows to enhance user’s wardrobe by automatizing the
personal-stylist recommendations of new outfits (i.e.
emulating the AI stylist), while keeping the human in
Naive Bayesian Network for Automated, Fashion Personal Stylist
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