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From Sean Owen <>
Subject Re: Getting started
Date Thu, 14 May 2009 16:16:39 GMT
Good, thanks for the report.

For such a small data set, you might get funky results with any
algorithm. Slope one is generally good when you have relatively few
items compared to users, or, need to update the data quickly at
runtime. Which algorithm gives the best-quality results is harder to
predict, and depends a lot on your domain and the quality of your
input data.

What you describe is the classic recommendation problem indeed. Any of
the algorithms in the package, in theory, solve this problem. I do
think slope one is a good place to start, in any event, because it is
so simple. You can try other algorithms next to see if they produce
better recommendations.

This is the forum to continue that discussion -- ready to consult here
as needed.


On Thu, May 14, 2009 at 5:01 PM, Burnett, Adam <> wrote:
> I updated and the exception is gone. Thanks for the quickness!
> The way my data is currently organized is I have 2 users that rated 10 different items.
There is an 11th item they both rated highly.  Ratings are normalized to be between 0 and
1.  When I ask for recommendations for user A I want items other users who share similar
tastes as A have rated highly, but that A has not yet rated. In other words, user A and B
both like item 1 and 2, maybe A will also like item 3 since B likes it too.  Is slope one
the correct algorithm to use in the case?

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