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I'm working with similar data sample as groupon site. I'm triyng to design best algorithm for such domain. Existing approaches seems will not work. For example item-to-item recommendation will not work due to short life of each deal - model will work only for several days. Can you point me to some articles related to recommendation in similar domains? Training Data: Anonymous user profile, deals profile and transactions. Target: Build ranking function for each user - i.e. personalisation of deals to users based on their history transactions
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Can you give a bit more detail? What data to you have, and what are you trying to predict?
In his new online lectures, Andrew Ng has a good explanation on how to rank for user preferences. Instead of using vectors of features for each Deal, you could take a different approach and use the users as features
Don't have time for a detailed answer right now, so a few notes. The problem you're facing is called a "warm start". That is, you want to start making good suggestions before you've collected a lot of data about the items you're offering. To do this you might consider the following:
Hope that helps.
Probably can do a lot worse than reading this:
http://pages.cs.wisc.edu/~beechung/icml11-tutorial/
@Noel thanks those resources cover topic very well
I don't understand the negative votes on this question - while the title may see overly specific application focused (which deserves downvotes), the actual question is not and is well defined. Anyone have any thoughts?