I've actually been planning to write about it (the actual algorithm, or at least the basic concepts there) and put it up for a while now, but finding time for writing is pretty hard....
Here's my best shot at a short technical summary of the concepts that are relevant and how they come into play:
1. Product information: You can easily give a score to how much information (in the information-theory sense) and interest in product gives on the random variable that describes what that user is likely to buy. This is easy and straight forward.
2. Purchase characteristics - Instead of looking at what a user is likely to buy as one complex random variable. It's much better to split it into a set of variables, that can be easily filtered\sorted by. Price range is one, but things like style preference are more complex. You'd want to know how informative each product is in relation to each of these.
3. The more difficult problem is figuring out how to effectively split this huge random variable into useful and simpler random variables. This is probably the most interesting part of our research, and where we've had the most use for deep learning.
As for an example: I'd probably look to the second page to find a good example. If we already know a user is looking for relatively expensive high-end watches - which products do we show them that will teach us about other things (specifically optimize on information on other aspects that aren't price)...
I hope this is clear enough - I really should at some point write this down more clearly...
Here's my best shot at a short technical summary of the concepts that are relevant and how they come into play:
1. Product information: You can easily give a score to how much information (in the information-theory sense) and interest in product gives on the random variable that describes what that user is likely to buy. This is easy and straight forward.
2. Purchase characteristics - Instead of looking at what a user is likely to buy as one complex random variable. It's much better to split it into a set of variables, that can be easily filtered\sorted by. Price range is one, but things like style preference are more complex. You'd want to know how informative each product is in relation to each of these.
3. The more difficult problem is figuring out how to effectively split this huge random variable into useful and simpler random variables. This is probably the most interesting part of our research, and where we've had the most use for deep learning.
As for an example: I'd probably look to the second page to find a good example. If we already know a user is looking for relatively expensive high-end watches - which products do we show them that will teach us about other things (specifically optimize on information on other aspects that aren't price)...
I hope this is clear enough - I really should at some point write this down more clearly...