This is, roughly, a reasonable thing to do. If you want to maintain the fiction of counts a little bit more closely, you might consider just having counts decay over time and having short visits only give partial credit.
On Wed, Jul 21, 2010 at 3:54 PM, Dave Williford <[email protected]>wrote: > 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. >
