On Wed, Jun 15, 2011 at 9:27 PM, aaron barnes <aa...@stasis.org> wrote:
> I'm thinking this still most closely resembles a 'boolean' model, because > it's not a matter of the user assigning a rating to every purchase, so we're > not looking primarily for users who have given similar ratings to similar > items, but rather giving extra weight to items that a user particularly > likes. > I recommend just using a cutoff and considering the rating to be binary. > > Am I on the right track here? Any advice on whether it would be better to > modify LogLikelihoodSimilarity to add an additional multiplier when a user > has 'liked' something? or is there a better preference based similarity > model i should use that would still give me a similar effect to a boolean? > I would avoid this. But the first step is to look at your data critically. How much rating data do you actually get? How much other data?
