On Tue, May 24, 2011 at 11:31 AM, Uwe Reimann <[email protected]> wrote: > Probably depends on how many data point were available before. I suspect > i.e. the 5th data point having a greater impact than the 105th. Is there a > lower limit (above 1) on the number of data points a user must have before > recommendations make sense?
Right. There's not one answer to that. A few data points can be meaningful enough to make recommendations from though more is generally better. > I did some testing of the different recommenders on a real data set from a > bookmarking site. GenericBooleanPrefItemBasedRecommender did not work very > well for me. It seemed to recommend the top links. Using > GenericUserBasedRecommender worked way better (after some tweaking), which > recommended links that actually fit my interests. Might need to do some more > testing here. Were you using "compatible" similarity implementations? Pearson is meaningless on boolean data and you would get poor results. Or -- there is GenericItemBasedRecommender, which does use ratings, and Pearson is fine with this implementation. > (1) would include categories, that should not be recommended, that's why (2) > is being used to pick the recommendations from. (2) would contain the liked > items of every user, that includes items that are disliked by other users. > (3) is for filtering out items that the user has not rated, but has been > presented before. I see. Yes it's entirely possible to compute user-user or item-item similarity on one model, and then apply those similarities to a recommender based on another model. (3) doesn't need a DataModel per se, but yes needs access to some list of previously-seen items in some form. up to you.
