This week we unveiled Polyvore for your Home, a major milestone in our 6-year history! Even though Polyvore is the largest fashion community on the web, our original prototype started in interior design, so we’ve always built our technology as a platform, designed to scale beyond fashion. That said, some parts of launching home were easy, some were hard, and there’s still plenty more work to do!
The Easy Parts
Because we had planned ahead (really far ahead, in some cases), some things were easy to build on top of our existing infrastructure.
Getting products in
We needed to bootstrap the home category with enough great products for our users to play with. Our data pipeline starts with a large network of crawlers running on EC2 that pull products from popular retailers like Nordstrom, West Elm and Fab. This is when we retrieve brand, price, availability, and more. The same technology we use on fashion sites works just as well on home sites, so it was just a matter of creating new crawler scripts to pull products from home retailer sites and feed them into our existing infrastructure.
Ranking home items
Every object in Polyvore is ranked. However, our scoring algorithm depends heavily on engagement data from our user community of tastemakers. In order to generate enough data to tell us what’s popular, we ran contests. That data allows us to generate our daily Top Home Sets and Top Home Products collections. As the home category grows, our ranking will improve.
We pride ourselves on delivering a delightful user experience (it’s one of our core values), which means spending time on getting even small details right. Luckily, for the Home launch, features like removing the background from an image and sending sale notifications were already built to handle multiple verticals.
The Hard PartsOf course, like most things worth building, not everything was easy. We started laying groundwork in 2011 because we knew some pieces of the launch would require a lot of time.
Our existing framework for classifying products uses a training set of items for each category, e.g. dresses, pants, furniture, rugs. Sounds like this would work for home products too, but we soon discovered the results for home categorization were not up to our high standards. Why? Because the home taxonomy is so much larger and more diverse than fashion’s. A shirt is pretty easy to categorize by keyword because most will have the word “shirt” or one of a few synonyms (“top”, “blouse”, “tee”) somewhere in the product title or description. But home items range from collectible frog figures to hardwood flooring to dog-shaped pillows to sleigh beds. Lots of wacky stuff! We ended up using much larger training sets than we do for fashion to reduce the noise and categorize more accurately.
New ambiguities in search
When we brought fashion and home into one experience, search queries that only had one meaning before became ambiguous. “Glasses” used to always mean eyewear, but now that query could be referring to drinking glasses.
Home products can also be made up of separate buyable parts. The phrase “brass knob” doesn’t tell us if the user is searching for the knobs themselves or for furniture pieces with brass knob details, like cabinets or dressers.
When a user has an ambiguous query, selecting the correct category to pull results from becomes more difficult. We ended up returning products depending on which category had the best results, but this is something we will continue to tune as we get more data.
Tuesday’s announcement was merely the launch of Polyvore Home v1.0, and great products should continuously evolve and get better. This means our work is hardly done!
New ways to browse and filter
Discovering new products by category worked great for fashion, but home shoppers expect to be able to browse by room, so we’re working on associating products with the rooms they belong in. There is also an additional complication of home items having a range of prices--the same sheet set in California King size is going to cost more than the Twin. This wasn’t as big of a problem in fashion since most items have one price and when there is a range (regular vs. petite sizes, for example), the difference is smaller. Being able to show our users the correct price ranges will be a better shopping experience, so we’re extending our platform to support it.
Growing a community
Our fashion-focused users have grown to 20 million strong, but the home community still has a ways to go. We’re leveraging our experience growing our fashion user base to grow our home community as well as trying out some new experimental methods to (hopefully) accelerate our expansion. But that’s a whole ‘nother blog post, so stay tuned!