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March
12

Introducing Project Emporia Powered Matchbox Technology: News is Now Yours

When discussing relevance in search, people often cite personalization as the next frontier in delivering more relevant results. Traditional methods of personalization have been based on something called ‘collaborative filtering.’ At its simplest, ‘collaborative filtering’ means if user A and user B both like something, then you can predict user A’s future “likes” based on what user B “likes.” 

This approach works quite well for things that are static in nature such as products, restaurants and movies.  For these objects, systems are able to gather enough rating data to make future predictions because the objects don’t change all that often.  Contrast that to the frenetic pace of news and real-time information where a story can materialize in a matter of minutes. How do you approach personalization in a reality where there will not be enough activity to enable collaborative filtering systems to kick in?

Today, at SXSW Interactive in Austin, TX, we showcased a new approach to personalization. Rather than looking only at what others like you are interacting with on the web, the Matchbox technology begins to ‘understand’ the Web more like a human might. This enables us to make predictions about what you might want to see, not only based on if someone like you has done something with it but also on what the content says. 

As an example: let’s say over the past several weeks you have been reading and rating articles on electric cars. Traditional collaborative filtering techniques would look at other people who have read similar articles and cluster you with them.  So when a new article comes out about Tesla’s new model, as long as someone ‘like you’ interacts with it, it might get recommended to you. But how does that first ‘someone like you’ find it in the first place?  That’s where Matchbox can help. 

Rather than simply relying on the actions of others, the Matchbox technology uses the wealth of information about the entities mentioned in an article. In this case, Matchbox knows that Tesla is an ‘electric car’.  With this additional information, it can display “Tesla Model S: 300 miles on 1 charge" to you without having to receive input from other like-minded individuals.  For those geeking out, this technique is called ‘feature generalization’ and is a key underpinning of Matchbox.

Project Emporia is the first broad implementation of the Matchbox technology that you can use.  Project Emporia is a new web application and a Windows Phone 7 application that recommends news stories shared on Twitter using the Matchbox technology. It combines three major pieces of technology:

  • Filter news stories by automatically predicted news categories

  • Deep social integration of Twitter so you can see what stories your friends and friends-of-friends are sharing as well as curate your Twitter network

  • Recommend news stories based on your personal preference votes

This new approach will help users getting on top of the wealth of news coming at them every day. 

Ralf Herbrich

Principal Dev Manager, Bing Mobile Personalization 

 

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