Using Supervised Learning to Classify Clothing

Brand Styles

C. David Kreyenhagen, Timur I. Aleshin, Joseph E. Bouchard, Adam M. I. Wise, and Rachel K. Zalegowski University of Virginia, cdk3jf, tia4kj, jeb5ne, amw6mc, rkz3sd@virginia.edu

Abstract - Machine learning techniques have the

potential to alter the highly competitive online fashion

retail industry by improving customer service through

personalized recommendations. A fashion style

classification system can improve the customer search

functionality and provide a more personalized

experience for the user. Supervised learning techniques

with fashion based applications face the problem of

developing quantitative measures for describing fashion

products which are subjective in nature. To address this issue the authors asked fashion experts to assist in the

assembly of a training set of brand-style associations.

Quantitative measures were attributed to each brand in

the training set by applying natural language processing,

text mining, and eBay query results. This data set was

used to train a support vector machine which classified

the approximately 8000 remaining brands into style

categories. The prospective classifier model was assessed

based on its positive predictive values which yielded a

56.25% success rate. Given that there are eight different

styles to choose from, a baseline for the percentage is

only 12.5%. The SVM thus adds significant value to the

classification of fashion brands. The final style

categorization was integrated as a new filter feature that

allows the user to narrow down their searches and

access relevant results.

Index Terms – Fashion, Machine learning, Supervised

learning, Support vector machines, Text mining

INTRODUCTION

Since a shopper’s purchasing decisions are often influenced

by the opinions of others [1], many customers seek the

assistance of a personal shopper in order to stay up to date

with today’s fashion styles and trends. Although these personal shoppers provide a helpful, unbiased opinion,

many customers are too busy, or cannot afford, to utilize

personal shoppers that are offered at popular department

stores.

Best Fashion Friend (BFF), a startup social fashion

application based out of New York City, NY, seeks to

provide a shopping experience comparable to that of a

personal shopper by incorporating social media aspects and

personalized shopping into a web application. BFF allows

users to do many things including but not limited to:

shopping for specific items of clothing, designing looks that

involve multiple pieces of clothing, and asking for a friend’s

approval of clothing items and/or looks. By creating a social

network centered on fashion, the company hopes to drive

sales in the online fashion industry.

The goal of the authors, as consultants for BFF, is to

improve the search feature within the app. A fashion style

classification can improve the customer search functionality

and provide a more personalized experience for the user.

Categorizing search results by fashion style and

implementing a filtering system would allow users to

manually refine their search results. This manual filtering gives users greater control in finding merchandise that

interests them. Connecting users to more clothing they like

serves BFF’s higher-level objective of increasing the

number of purchases from their affiliate network of vendors.

BFF earns a commission on every affiliate network

purchase that occurs after a user finds clothing through the

app. Filtering also makes the app more useful and will allow

BFF to improve customer loyalty and attract new customers.

A more complete visual of these goals is shown in Figure 1.

FIGURE 1

BFF COMPANY OBJECTIVES

PROBLEM DEFINITION

I. Motivation for Personalization

Liang, Chen, and Turban found that the perceived

usefulness of a service is higher for personalized services

than for non-personalized ones [2]. Since the usefulness of a

service influences the amount of user traffic seen by that

978-1-4799-4836-9/14/$31.00 (c) 2014, IEEE 239

service, BFF can improve customer loyalty, increase the

user base, and attract new customers by providing the

opportunity for user personalization. It has been shown that

“non-monetary benefits such as convenience from online

personalization can also serve as incentives for consumers

to part with their personal and preference information” [3].

The BFF application already incorporates some social media

functions, such as allowing users to become “friends” with

one another to view each other’s fashion preferences. However, more personalization is desired, specifically in the

BFF Store feature. Therefore, the authors' goal is to improve

the personalization of the Best Fashion Friend search

functionality.

II. Recommendation System Approach to Personalization

Besides the use of social media functions, another way personalization may be incorporated into the BFF

application is through a personalized recommendation

system. A recommendation system is used to apply

knowledge and techniques to the problem of making

personalized recommendations during an interaction with a

user [4]. The most logical place to implement the concept of

a recommendation system is the BFF Store, where the

system would provide search results tailored to an

individual’s specific interests.

Most recommendation systems require at least some

information about the users, whether it be demographic data

or preferences. This is referred to as the "cold start"

problem. After delving deeper into BFF’s user data, the

authors determined that there was not enough useful data to

overcome this problem and create a recommendation system

of any significance.

III. Style-brand Classification Using Support Vector Machines

Introducing a filtering system circumvents the cold start

problem while improving the personalization of the BFF

Store search. In addition to basic filtering, the BFF user will

also be able to refine his or her search results based on

fashion style preferences. In order to create a style-based filter, the items in the BFF Store must first be classified. By

applying concepts of support vector machines (SVM) to the

item descriptions, the authors were able to create a method

to classify each brand according to its dominant style.

According to Martin-Barraga, Lillo, and Romo in the

European Journal of Operational Research, SVMs are

powerful tools that provide nonparametric classifications on

high-dimensional data [5]. According to a study performed

using SVMs for Science Direct Journal, due to their

simplicity and their embodiment of structural risk

minimization, SVMs are recommended as the standard

intelligence technique for classification [6]. It is their

simplicity and ease of use on high-dimensional data that

made SVMs the obvious tool to apply when creating style-

brand classifications to improve the user’s personalized

experience on BFF.

SYSTEM DESIGN

A style filter was created which allows users to refine their

search results based on fashion style. The filter relies on

eight lists of fashion brands. These lists are titled Active,

Bohemian, Classic, Edgy, Couture, Minimalist, Preppy, and

Outdoor. Each list includes the brands which are most

representative of these respective styles. The means by

which they were created and implemented are described

below.

I. System Architecture

The BFF Store function is a means by which users can

discover new clothing items, add items to their BFF closet,

and make clothing purchases. The user can find new items

by first inputting a search query. Members of the BFF

affiliate network are then searched for items matching the

query. Items are returned with information regarding brand,

price, color, and category. If the user purchases an item

from an affiliate, BFF is awarded a portion of the sale.

The new interface for the BFF Store allows users to filter their searches. Once they have searched for an item,

the results can be refined based on item attributes. Users

can also filter the results based on their style preferences

(Figure 2).

FIGURE 2

BFF STORE INTERFACE WITH STYLE FILTER

Since style is not an attribute passed down from the

affiliate network, style filtering must be done after the

results are on BFF servers. A supervised learning algorithm

was used to classify the approximately 8,000 fashion brands

in the affiliate network by rating them in each style

category. When a style is selected, products made by

brands that are the most affiliated with the selected style are shown to the user first. The filtering process is shown in

Figure 3.

II. Methodology

Each article of the clothing available from the affiliate

network is defined as a JSON object embedded with certain

characteristics, namely brand and a short text description of the item. Assuming that a clothing item’s style is correlated

to the item’s brand, styles can be assigned to a large set of

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FIGURE 3

FILTER SYSTEM ARCHITECTURE

clothes by classifying the smaller subset of brands. To

classify each brand as a member of one of the eight

predefined styles, each brand was treated as a point in a 16

dimensional space and evaluated by the support vector

machine. Eight of these dimensions measure the relevance

of a style using the tf-idf method of text mining [7].

Term frequency-inverse document frequency (tf-idf) is a statistical method to determine how important an

individual word or phrase is to discerning the uniqueness of

a block of text. For each brand, a block of text

characterizing that brand was created by extracting the

adjectives, adverbs, and nouns from the name and

description of every item in the affiliate network that

belongs to that brand. These text blocks were fed into the tf-

idf algorithm which determines the frequency of each word

in the brand’s text block relative to the frequency of that

word in the global corpus of all text blocks. This is

calculated into a tf-idf statistic, which can be used to weigh

the relative importance of that word in assigning a style to a

brand. This method is commonly used in search engines to

determine how relevant a webpage is based on its text

content given the search query.

The two metrics involved in calculating the tf-idf

statistic are term frequency and inverse document frequency. Term frequency is the frequency of a word in a

document, or the local importance to the individual text

block. The inverse document frequency measures the global

relevance of a word compared to the complete text corpus,

which consists of the text blocks of all the evaluated brands.

The tf-idf statistic is the product of the term frequency and

the inverse document frequency.

Term Frequency: occurrence of word in document

Inverse Document Frequency:

Where N is the total number of brand descriptions in the

corpus.

The tf-idf algorithm produces a value of 0 to 1 for each

brand-style association, 1 meaning the brand is likely to be

associated with the given style. Since there are eight styles,

the output creates a point for each brand in an eight

dimensional space. Another set of eight dimensions are

added by determining the brand-style associations resulting

from eBay searches.

The eBay brand-style associations are calculated by

taking the fraction of two search queries. The returned

search result count from the first query, which contains the

brand and the style, serves as the numerator. The denominator is purely the search result count of the query

containing the brand only. When calculated for all styles the

resulting set of eight fractions complete the 16 dimensional

point for a specific brand. The data structure of these points

is shown in Figure 4. Each brand’s point is then used for

the SVM.

FIGURE 4

BRAND DATA STRUCTURE

The SVM uses a training set of 65 brands which have

been expertly classified and associated with one of the eight

different styles. A test set of 32 brands is then used to

evaluate the SVM classification of each brand. The metric

for successful classification of the brands is the percentage

of total brands that are classified correctly by the SVM,

which is referred to as the percentage of positively predicted

values.

III. Support Vector Machine Definition

The support vector machine used for style classification is a

supervised learning algorithm. It takes as input a training

set of 97 brand of the form

where is each brand, as represented by a point in the

space . is a 16-dimmensional space with dimensions

corresponding to the eBay and tf-idf data. is the

predetermined expert classification of each brand from a set

of styles,

The SVM solution is a set of 7 hyperplanes which divide the space as follows

(1)

Where is a unit vector , such that . is

the margin to the nearest point, as shown in Figure 5 (Hastie

p. 418).

Affiliate Network

Filter

Price

Color

Brand

Category

BFF Servers

Filter

Style User

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FIGURE 5

MARGIN CALCULATION USED IN DETERMINING HYPERPLANE FIT

A radial basis function was chosen for the calculation of

each hyperplane in (1), and is implemented by

determines how influential any single point is.

can be determined by solving for any

. Parameter determines the smoothness of

the boundary. If is low, then will be low, and the

boundary will be smooth.

The classification rule for any point is

(2)

The optimization function for the SVM is formalized as

(3)

where . The optimization allows for soft margins,

which allows misclassified points to be separated from their

intended subspace. is an expression which penalizes

misclassification. The optimization attempts to draw hyperplanes with margins as wide as possible, while

minimizing the number of misclassifications. A simplified

visualization of this algorithm is shown in Figure 6 (Hastie

p. 21). A visualization using actual data for Outdoor and

Preppy eBay search data is shown in Figure 7.

FIGURE 6

HYPERPLANE EXAMPLE USING RADIAL BASIS KERNEL FUNCTION AND

SOFT MARGINS

FIGURE 7

HYPERPLANE EXAMPLE USING ACTUAL DATA FOR OUTDOOR AND PREPPY

STYLE DIMENSIONS

RESULTS AND DISCUSSION

Our results yielded 56.25% positively predicted values.

Given that there are eight different styles to choose from a

baseline for the percentage is only 12.5%. The SVM thus

adds significant value in association with natural language

processing to the classification of fashion brands.

Though it is a step in the right direction, 56.25% is not high enough to confidently deploy this methodology

directly as the only source of logic for a classification

system. There are certain factors of the experiment that

could be improved upon to yield better results. Improving

the quality of the training set would be the most influential.

The authors have limited knowledge of the existing fashion

brands and despite the assistance of fashion experts, the

brands style associations and the chosen brands could be

further refined. The size of the training set could also be

increased.

I. Future Work

The primary objective of the project was to personalize the

BFF user experience by allowing users to select styles that

appeal to them. In pursuit of this objective the team was

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able to classify clothing items into a set of styles by using

machine learning, natural language processing, and text

mining techniques. A recommendation system for BFF

would be the next step of personalization.

Most recommendation algorithms require a moderate

amount of information about users. In order to combat the

cold start problem, some fashion applications require the

user to pick from certain brands and answer questionnaires

to gauge their initial preferences. This technique is similar to what companies like Pandora and Netflix do for

entertainment recommendation.

One methodology for gathering user preferences uses

conditional decision trees, by selecting one option as your

primary preference the next group of options presented is

based on the original decision. This process repeats with the

objective to not necessarily find the best alternative, but to

best categorize the user for future recommendation

purposes. Each conditional probability of selecting a

particular option uses Bayesian forecasting to formulate the

weights of the probabilities of belonging to a user category.

Decision trees are not the only solution to for building out

this type of system, but just one proposed alternative. The

authors suggest further research into algorithms that utilize

machine learning and leverage the style-brand classification

knowledge already gathered.

CONCLUSION

Best Fashion Friend seeks to provide a shopping experience

comparable to that of a personal shopper by incorporating

social media aspects and personalized shopping into a web

application. One key challenge presented by this goal is the

difficulty of showing users clothes that match their style

preferences. To this end, the approximately 8,000 clothing

brands in the site’s affiliate network were classified into

eight unique styles. This allows users to look for clothes that specifically adhere to their personal taste.

A Support Vector Machine was created to address the

classification task. It took search result measures from eBay

and item description text as input for each brand. Given a

training set of 97 brands, it can now classify new brands by

style. Results from the algorithm evaluation were

promising. They yielded 56.25% positively predicted

values, well above the baseline of 12.5%. This indicates

that supervised learning is a valid approach to style

classification. Higher success rates could likely be realized

by acquiring more data and combining multiple machine

learning algorithms. These results imply that the application

of machine learning has significant value in the highly

subjective topic area of women’s fashion.

ACKNOWLEDGMENT

The authors would like to thank Professor Alfredo Garcia of

the Department of Systems and Information Engineering at

the University of Virginia. They would like to thank all of

the employees of Best Fashion Friends. They would like to

thank the survey respondents. The authors take full

responsibility for the ideas, concepts, and opinions

presented here.

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