On Wed, Jul 28, 2010 at 1:02 AM, Tanton Gibbs <[email protected]>wrote:
> Another thing to consider in this same vein is that 1 or 2 clicks on a > resource may indicate a very strong preference (if the topic is > generally unpopular) or it may indicate a very weak preference (if the > topic is highly popular). You should consider how other users are > interacting with this and other similar resources to help determine > satisfaction. > The recommendation system should handle this. The log-likelihood similarity thing-thing is the one I would recommend starting with. > > I'll also echo Ted's comment that clicks are just a proxy for user > satisfaction. If you have a better way to measure satisfaction (such > as time spent with the resource, further interaction, etc...) then you > will end up with better recommendations. > I should emphasize again that this is a much stronger effect than most people realize when I tell them that this is a good thing to do. It can easily make the difference between complete hash and extremely good recommendations. Picking the right action to analyze can easily make more difference than any possible algorithm choice. In addition, picking a good indicator of interest can easily decrease the amount of clicks to analyze by up to an order of magnitude. This is nice.
