Amazon Fashion Discovery Engine

1 minute read

Problem statement

To recommend similar apparel products in e-commerce using description and images

Data Source

Amazon Product Advertising API

The extracted json file has following focused features -

Brand
Color
Product type name
Medium image URL
Title
Formatted price

Exploratory Data Analysis

Number of data points ~ 183K

Missing data

We targeted the products whose price are missing and drop the products, in favor of reducing the dataset to 28K data points.

Checking on duplicates,

We have 2325 products which have same title but different color. So we can drop these and keep only the uniques here.

There are also 2 possibilities of dupes like -

First possibility (title differ in end) -
woman’s place is in the house and the senate shirts for Womens XXL White
woman’s place is in the house and the senate shirts for Womens M Grey

Second possibility (only few words differ in title) -
UltraClub Women’s Classic Wrinkle-Free Long Sleeve Oxford Shirt, Pink, XX-Large
UltraClub Ladies Classic Wrinkle-Free Long-Sleeve Oxford Light Blue XXL

We remove such possibilities as well as those titles whose length is less than 4 words, leaving us to 16K data points.

Stopwords and Stemming

We removed possible stopwords and implemeted Porter Stemmer on title feature.

Product similarity model using text features

We implemented various similarity models using only Title feature, as listed -

Bag of Words
TF-IDF
IDF
Word2Vec
TF-IDF weighted Word2Vec
IDF weighted Word2Vec

While getting the results from each of these methods, the last one presents the best similar products.
We further combined weighted Color and Brand features along with Title feature to increase the similarity.

Input text (using only title) -
burnt umber tiger tshirt zebra stripes xl xxl

Recommended products -
pink tiger tshirt zebra stripes xl xxl
brown white tiger tshirt tiger stripes xl xxl
grey white tiger tank top tiger stripes xl xxl

Product similarity using Images

We used VGG16 (a type of CNN architecture) to find similar apparels implemted in Keras backed by Tensorflow.

# dimensions of our images.
img_width, img_height = 224, 224
nb_train_samples = 16042
epochs = 50
batch_size = 1

# the weights are taken from imagenet