Code as Craft: Understand the role of Style in e-commerce shopping
Aesthetic style is key to many purchasing decisions. When considering an item for purchase, buyers need to be aligned not only with the functional aspects (e.g. description, category, ratings) of an item’s specification, but also its aesthetic aspects (e.g. modern, classical, retro) as well. Style is important at Etsy, where we have more than 60 million items and hundreds of thousands of them can differ by style and aesthetic. At Etsy, we strive to understand the style preferences of our buyers in order to surface content that best fits their tastes.
Our chosen approach to encode the aesthetic aspects of an item is to label the item with one of a discrete set of “styles” of which “rustic”, “farmhouse”, and “boho” are examples. As manually labeling millions of listings with a style class is not feasible – especially in a marketplace that is ever changing, we wanted to implement a machine learning model that best predicts and captures listings’ styles. Furthermore, in order to serve style-inspired listings to our users, we leveraged the style predictor to develop a mechanism to forecast user style preferences.
Style Model Implementation
For this task, the style labels are one of the classes that have been identified by our merchandising experts. Our style model is a machine learning model which, when given a listing and its features (text and images), can output a style label. The style model was designed to not only output these discrete style labels but also a multidimensional vector representing the general style aspects of a listing. Unlike a discrete label (“naval”, “art-deco”, “inspirational”) which can only be one class, the style vector encodes how a listing can be represented by all these style classes in varying proportions. While the discrete style labels can be used in predictive tasks to recommend items to users from particular style classes (say filtering recommended listings to a user from just “art-deco”), the style vector is supposed to serve as a machine learning signal into our other recommendation models. For example, on a listing page on Etsy, we recommend similar items. This model can now surface items that are not only functionally the same (“couch” for another “couch”) but can potentially recommend items that are instead from the same style (“mid-century couch” for a “mid-century dining table”).
The first step in building our listing style prediction model was preparing a training data set. For this, we worked with Etsy’s in-house merchandising experts to identify a list of 43 style classes. We further leveraged search visit logs to construct a “ground truth” dataset of items using these style classes. For example, listings that get a click, add to cart or purchase event for the search query “boho” are assigned the “boho” class label. This gave us a large enough labeled dataset to train a style predictor model.
Once we had a ground truth dataset, our task was to build a listing style predictor model that could classify any listing into one of 43 styles (it is actually 42 styles and a ‘everything else’ catch all). For this task, we used a two layer neural network to combine the image and text features in a non-linear fashion. The image features are extracted from the primary image of a listing using a retrained Resnet model. The text features are the TF-IDF values computed on the titles and tags of the items. The image and text vectors are then concatenated and fed as input into the neural network model. This neural network model learns non-linear relationships between text and image features that best predict a listings style. This Neural Network was trained on a GPU machine on Google Cloud and we experimented with the architecture and different learning parameters until we got the best validation / test accuracy.
As described above, the style model helps us extract low-dimension embedding vectors that capture this stylistic information for a listing, using the penultimate layer of the neural network. We computed the style embedding vector using the style model for all the listings in Etsy’s corpus.
Given these listing style embeddings, we wanted to understand users’ long-term style preferences and represent it as a weighted average of 42 articulated style labels. For every user, subject to their privacy preferences, we first gathered the entire history of “purchased”, “favorited”, “clicked” and “add to cart” listings in the past three months. From all these listings that a user interacted with, we combined their corresponding style vectors to come up with a final style representation for each user (by averaging them).
Building Style-aware User Recommendations
There are different recommendation modules on Etsy, some of which are personalized for each user. We wanted to leverage user style embeddings in order to provide more personalized recommendations to our users. For recommendation modules, we have a two-stage system: we first generate a candidate set, which is a probable set of listings that are most relevant to a user. Then, we apply a personalized ranker to obtain a final personalized list of recommendations. Recommendations may be provided at varying levels of personalization to a user based on a number of factors, including their privacy settings.
In this very first iteration of user style aware recommendations, we apply user style understanding to generate a candidate set based on user style embeddings and their latest interacted taxonomies. This candidate set is used for Our Picks For You module on the homepage. The idea is to combine the understanding of a user’s long time style preference with his/her recent interests in certain taxonomies.
This work can be broken down into three steps:
- For each user, obtain top three styles and three latest taxonomies.
Given user style embeddings, we take top 3 styles with the highest probability to be the “predicted user style”. Latest taxonomies are useful because they indicate users’ recent interests and shopping missions.
- For each (taxonomy, style) pair, generate 100 listings.
Given a taxonomy, sort all the listings in this taxonomy by the different style prediction scores for different classes, high to low. We take the top 100 listings out of these.
- For each user, remove invalid (taxonomy, style) pairs.
Taxonomy, style validation is to check whether a style makes sense for a certain taxonomy. eg. Hygge is not a valid style for jewelry.
- For each user, aggregate all listings generated by each valid style & taxonomy pair and take top 200 listings with the highest average purchase and favorite rate.
These become the style based recommendations for a user.
We were extremely interested to use our style model to answer questions about users sense of style. Our questions ranged from “How are style and taxonomy related? Do they have a lot in common?”, “Do users care about style while buying items?” to “How do style trends change across the year?”. Our style model enables us to answer at least some of these and helps us to better understand our users. In order to answer these questions and dig further we leveraged our style model and the generated embeddings to perform analysis of transaction data.
Next, we looked at the seasonality effect behind shopping of different styles on Etsy. We began by looking at unit sales and purchase rates of different styles across the year. We observed that most of our styles are definitely influenced by seasonality. For example, “Romantic” style peaks in February because of Valentines Day and “Inspirational” style peaks during graduation season. We tested the unit sales time series of different styles for statistical time series-stationarity test and found that the majority of the styles were non-stationary. This signifies that the majority of styles show different shopping trends throughout the year and don’t have constant unit sales throughout the year. This provided further evidence that users tastes show different trends across the year.
Using the style embeddings to study user purchase patterns not only provided us great evidence that users care about style, but also inspired us to further incorporate style into our machine learning products in the future.
Etsy is a marketplace for millions of unique and creative goods. Thus, our mission as machine learning practitioners is to build pathways that connect the curiosity of our buyers with the creativity of our sellers. Understanding both listing and user styles is another one of our novel building blocks to achieve this goal.
For further details into our work you can read our paper published in KDD 2019.
Authors: Aakash Sabharwal, Jingyuan (Julia) Zhou & Diane Hu
Posted by Nishan Subedi on 12 Jul, 2018
Posted by Mohit Nayyar on 01 May, 2017
Posted by Daniel Marks, Bill Ulammandakh and Sungwan Jo on 25 Jan, 2017