What do you mean that you have a user-item association from a log-likelihood metric?
Combining two values is easy in the sense that you can average them or something, but only if they are in the same "units". Log likelihood may be viewed as a probability. The distance function you derive from it -- and your own TFIDF distance -- it's not clear if these are comparable. Rather than get into this, I wonder whether you need any of this at all, since I'm not sure what the user-item value is to begin with. That's your output, not an input. On Thu, Mar 22, 2012 at 9:18 PM, Ahmed Abdeen Hamed <[email protected]> wrote: > Hello, > > I developed a recommender that computes the distance between two items > based on contents. However, I also need to include the association between > the user-item. But, when I do that, I end up having a similarity score from > the item-item content based and also another similarity score based on the > item-user association (loglikelihood). I am now designing some experiments > to consider different weights for each approach before I add them together. > Here is the mathematical model what I have in mind: > > LOGLIKELIHOOD_WEIGHT*(1.0 - 1.0 / (1.0 + logLikelihood)) + > (CONTENT_WEIGHT* content-proximity) such that > > [1] LOGLIKELIHOOD_WEIGHT (weight between 0, 1 e.g., 0.6) > > [2] CONTENT_WEIGHT (weight between 0, 1 e.g., 0.4) > > [3] logLikelihood is a variable that gets populated by a logLikelihood > similarity metric based on the user-item association > > [4] content-proximity is variable that gets populated by > a contents-based similarity algorithm (TFIDF). > > My question now is: Does this mathematical model make sense? Can we add the > two different scores even though they are from two different distributions > the way I did above or the outcome will be skewed? > > Please let me know if you have an answer for me. > > Thanks very much, > > -Ahmed
