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From Sean Owen <>
Subject Re: Theory question about Pearson Correlation and user based recommender
Date Wed, 09 May 2012 16:20:34 GMT
True. But that neighborhood of more-similar users is smaller, and the
things they know about are fewer. Maybe better recommendations are found
attached to users a bit farther away, that are not recommendable when
considering only a very small and close neighborhood.

On Wed, May 9, 2012 at 5:13 PM, Daniel Quach <> wrote:

> 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.

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