We are currently using LogLiklihoodSimilarity to create item recommendations based on page visits on our web site. We would like to influence the generated recommendations for such factors as age of visit (weigh more recent visits more heavily), duration of page view (longer is better), same visit is better than cross-visit (things looked at on the same day are more related than items looked at by a given user across visits).
I am considering introducing scores for each user/page data point. This would essentially replace the integer calculations (which are based on summing total data points for each item, total items, and the intersection of item A with item B) with real numbers. We could always round the sums to integers before sending through the loglikelihood calculation although I am not sure this is necessary. Note these score are not the same conceptually as preferences so I don't think switching to a preference based algorithm would give satisfactory results. I am very new to all of this and am wondering if I am completely off base or if this seems like a valid approach. Any input is much appreciated.
