I am running average absolute difference evaluations of a generic user based recommender that uses a threshold based neighborhood and pearson correlation to determine similarity.
I evaluated several recommenders for varying minimum thresholds for the neighborhood (0.9, 0.8, 0.7, 0.6, 0.5) I noticed that as I decrease the threshold, the average absolute difference actually goes down, from: 0.85299 difference at 0.9 threshold of similarity to 0.77667 difference at 0.5 threshold of similarity My original intuition was that a higher threshold of similarity should result in more similar users appearing in each neighborhood, and hence should result in lower average absolute differences. However, this does not appear to be the case. Is there possibly some theoretical reason behind this? I repeated the same experiments using uncentered cosine similarity and those results reflect my original intuition (decreased difference when minimum thresholds for neighborhoods are higher) I am performing experiments over the movie ratings from group lens.
