- Stream 1: Generates recommendations based on a user’s brand-affinity (a passion for Prada, for example).
- Stream 2: Generate recommendations based on collaborative filtering: items that similar users have liked.
- Stream 3: Leverages the talent of our awesome community of creators by recommending items frequently paired in sets.
Friday, February 27, 2015
Wednesday, January 7, 2015
When we first started building each user’s Style Profile, we quickly realized how tricky quantifying fashion can be. It’s intangible, means different things for different people and even when most people might own the same black shirt, they might wear it in completely different ways. Luckily, Polyvore is uniquely positioned to understand personal style through our users rich interactions on Polyvore, including:
- Global factors: occasions, trends, seasonality and other contextual information
- Catalog data: rich and high-quality metadata of products from our retail partners
- Product data: product likes and dislikes, collections of products, products viewed and search queries
- Shopper behavioral data: impressions, likes, outbound clicks while they are interacting with products, sets and other curated content
- Community data: Our global community has generated billions of data points that helps us understand the relationship between retail products. Every time a user creates a set, they are implying that those products go together and share the same style.
From a technology standpoint, a user’s Style Profile can be represented with a vector in a high-dimensional space and the component for each dimension, indicating the strength of their preference in a particular aspect or a combination of multiple aspects in fashion. The following is a simplified representation of two users’ style profile on combinations of color, category, material and brand:
|Figure 1: Style profiles|
- Style Space Definition: a high dimensional space where any point represents the style of a user or product that is subject to constraints that points with similar style should be closer to each other than those with different tastes.
- Style Vector Definition: the coordinates in the Style Space denote the taste vector for that particular user or product.
Thursday, December 11, 2014
As we approach the end of 2014, Andrea talks about how she analyzed Polyvore search queries to get a pulse on the community over the past year. To learn more about Andrea, check out her About Me set here. And to learn more about Polyvore’s end of year search data, check out our blog post here.
What do you do every day?
I have a fascinating job! Every day I spend time reviewing Polyvore’s search query logs to find notable patterns in Shop search, Set search, and Editor search, and then I share insights about these trends with the rest of the Polyvore team.
Friday, November 21, 2014
Making ThingsKurt Schaefer is our Junkyard Mega-Wars star and a Staff Engineer who works on making our iOS apps amazing. Since working here, Kurt has learned both to code in Objective-C and how to mount an effective foosball defense (so key!). Kurt is an incredibly creative craftsman of the Maker-verse who sews amazing Halloween costumes from scratch, and loves building whimsical projects in his workshop while listening to Ella Fitzgerald. Read more about his projects at his blog, Retro Tech Journal, and check out his Polyvore set!
As an amature wood-worker, machinest, welder, and tinkerer, I'm constantly learning from my hobbies and applying what I learn in my role as a Software Engineer at Polyvore. I am passionate about making things. Here's some advice on making things both at home and professionally.
Why Make Things?
It turns out that it's fun to make thingsFrom building a custom door knocker to launching home brew rockets with your kids, building things is fun. It gives you a sense of accomplishment. Physical things have a heft and permanence. They're very accessible.
Tuesday, October 14, 2014
Get to know the Polyvore engineering team! Here, Esha talks about how she works with big data, our tech stack and what makes up our engineering culture.
What do you do every day?
I’m on the infrastructure team, so I help maintain our backend infrastructure in our Data Center and Cloud on AWS. In addition to my day job, I love getting updates on all the latest fashion trends from our creative community. It’s pretty cool that I’m able to help build a platform that empowers people all over the world to express their style.
How does Polyvore’s scale affect your daily work?
As our Polyvore community grows, my team continues to scale our backend systems. With new features built on our data, I help create new Cassandra clusters, and scale the existing Cassandra clusters by adding more nodes to accommodate our growing data. Cassandra is our backend for storing our unique outfit data for our newly launched Style Graph, which provides users with personalized product recommendations.
Tuesday, October 7, 2014
Today we’re excited to announce the launch of personalized recommendations on Polyvore for iPhone. You can download the app here!
Although the user experience of a personalized feed is simple and delightful, what goes on under the hood is quite complex. Personalization is no easy feat, but we were able to create hyper-personalized recommendations at scale that produce a 4x increase in product likes (one of our key measures of engagement).
Over the next few months, we’ll be doing a series of blog posts about how it all works. To get started, here's a quick infographic explaining the problem, our approach, and the results:
So, why is style such a hard problem?Style is complex and nebulous. It's intangible, highly personal, and constantly evolving. Think about how you would try to go about describing your style or the kind of image you want to project. Think about all the details that need to be taken into account: the occasion (wedding vs. vacation), time of year (winter vs summer), situation (work vs. weekend), all the way through to tiny details that you love or hate (rhinestones and fringe, anyone?). It's not a straightforward problem with clear rules that you can encode.
Wednesday, August 20, 2014
Hi, my name is Julia Alvarez and I am a junior at Brown University, pursuing a degree in computer science. You should know that I am not one of those programmers who has been coding since I was in diapers. In fact, I switched from psychology to international relations to English and finally to computer science.
I "discovered" computer science after stepping into the opening day skit of Brown’s legendary CS15, an "Introduction to Object-Oriented Programing" course, and was hooked. I found that the beauty of CS was that I could actually make something useful, all while sitting at my desk. I wanted to learn more and to practice this magical "making" process, so I started hunting down a summer internship.
I had a mentor who did not work at a tech company, but knew many people who did. When she told me she knew some people who worked at Polyvore, I got really excited, because I had not only used Polyvore before, but I really liked it, and really believed (and still do) they had huge potential for growth. When they called me and told me they would like to interview me, I of course freaked out and said yes right away.
It has been ten weeks since I began my internship, and I would like to share the 10 most surprising things I have learned during my time here.