+ Garrett French Talks to Greg Linden About Findory & Personalization (via SEL)
We’ll say it again, Findory.com is a very useful resource that we use regularly. Gary also interviewed Greg a couple of years ago.
One thing we’ve learned from him is the importance of simplicity for the end user.
Question: how would you describe or characterize the mathematics of recommendation? do you factor in length of page views or how long it takes to click back?
Answer: Findory recommends interesting articles based on what you read and what others have read.
It is a little like social networking sites, the sites where you list all your friends and then share information between the network of friends.
Unlike social networking sites, everything is done implicitly and anonymously. Rather than list your friends, other like-minded readers of Findory are found for you. Rather than explicitly share, interesting things others have found are quietly and anonymously shared behind the scenes.
All the hard work is done by humans. Findory readers find all the good articles. Findory only helps readers share what they have found easily and with no effort.
Technically, the algorithms used fall into the class of social filtering algorithms, though it often can be tricky work to get those types of techniques to scale to large data.
+ Windows Live QnA beta goes public!
More places for people to ask questions. Competition for Yahoo and Google Answers. What about virtual reference in terms of mind share for certain types of queries?
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