I'm doing some studies on the bias of recommender system, and using this
approach combined with
person correlation gives some very weird results. For example if I take
items that have a mean of less than 2.5, it
is more likely that those items are ranked higher than items which have
a very high mean (ie higher than 3.5). it took me a while to
figure out why, and the reason is the approach you take to calculate
prediction always biases the score towards the mean. So that I end
up with a very low variance for the predicted items compared to for
example SVD.
Tamas
On 22/02/2010 13:03, Sean Owen wrote:
It's a good question. The bigger question here is, how do you create a
weighted average when weights can be negative? That leads to wacky
results like predicting ratings of -5 when ratings range from 1 to 5.
My fix was to make all weights nonnegative in this way. If you ignore
items with similarity 0, what would you do with items with negative
similarity?
You could ignore them I suppose; it loses some key information, but
might be OK. It also presupposes that similarity 0 means no
resemblance at all; that's not necessarily what 0 means for similarity
-- at least in the context of this framework. While it means no
resemblance in the case of similarities built on things like the
Pearson correlation, it doesn't for other metrics.
Sean
On Mon, Feb 22, 2010 at 12:54 PM, Tamas Jambor<[email protected]> wrote:
hi,
Just wondering how you justify that you add +1 to the correlation, when you
calculate the score for the recommendation.
so that items which are not correlated constitute to the score. I think this
biases the recommender towards the mean of the ratings of the target users
(for item based),
Tamas
