93South – Thoughts on New England Web 2.0 - Promoting new technology within the Boston Area

Our next 93Startup Profile is from another MITX finalist and a company that I have covered on 93South before – StyleFeeder, the web 2.0 “personal shopping site”.

What do you think of StyleFeeder? Let Boston Know.

StyleFeeder
Location: Cambridge, MA
Funding: VC – ($2M Series A in Jan. 08 plus an earlier round)
# of Employees: 7
URL: http://www.stylefeeder.com
Blog: http://blog.stylefeeder.com,
http://blog.tech.stylefeeder.com
Submitted By: Dina Pradel

What’s your elevator pitch?

StyleFeeder combines the best of Web 2.0’s social and personalization aspects in the Web’s only personal shopping engine. By learning about each person’s unique sense of style, StyleFeeder helps shoppers discover new products they’ll love and others who share their tastes. Even search results are returned with the user’s predicted order of preference in mind, providing a much more efficient way to find products like clothes, furniture and gadgets. StyleFeeder has also created the largest shopping application on Facebook, helping social networkers share their latest shopping finds and access their personal recommendations within the Facebook experience.

Who is your target market?

StyleFeeder can help anyone shop better, as our recommendation engine is designed to show users only the people and products we think are going to appeal to them. That being said, we’re really popular among 18-to-30-year-old female power shoppers!

What makes you different or stand out?

There are a lot of sites that say they offer “recommendations” to users…but often, the recommendations are extremely generic, or the onus remains on the user to browse other people’s finds. Current search engines work well if you know exactly what you’re looking for, but what if you only have a vague idea of what you want? We’ve used technology to cut through the clutter and make it much easier to discover products that the unique individual that is you is going to like. Our preference algorithm allows us to recommend not only products from around the web, from major retailers to long-tail boutiques, but also people who share your similar sense of style. Our site is social, in the sense that we’re drawing on the wisdom of the many, but it’s the combination of that social data as interpreted by our preference technology that is really powerful in providing actual utility to shoppers. We’ve also managed to crack the “cold start” problem that has plagued other recommendation engines based on collaborative filtering techniques – we can incorporate new data from users in real time.

Who are the founders?

Philip Jacob, a software architect, technology consultant and veteran of several Boston-area startups, created the initial version of StyleFeeder in 2005 and now serves as the CTO.

The management team is rounded out by VP of Business Development, Shergul Arshad, an e-commerce veteran with experience at companies like eBay and Fairmarket, and VP of Marketing, Dina Pradel, who had a background in targeting young web-savvy users at companies including MTV/Viacom and Y2M: Youth Media and Marketing Networks.

What is your startup story?
(Where did the idea come from, inspiration, funny anecdote, etc., etc.)

The story is actually one of the sections of our site that gets the most comments from users: http://www.stylefeeder.com/about.html! The short version is that Phil noticed that his wife was having a difficult time Christmas shopping with her friends — they were finding great stuff online, but losing track of the links when they tried to email or IM them to each other. Phil developed a site that would allow users to bookmark the items they were shopping for, and that’s how StyleFeeder became the first site in what is now called the “social shopping” space.

But it soon became apparent that shoppers were hungry for more powerful product discovery methods; hence the hiring of Jason Rennie, who holds a PhD from MIT in collaborative filtering. Rennie developed various data analysis techniques to create a sophisticated recommendation algorithm that powers the personalization aspects of the site.

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