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From Nick Jordan <>
Subject Item Based Recommendation Evaluation based on Number of Preferences
Date Wed, 04 Jan 2012 01:51:26 GMT
Hi All,

I'm currently running an item based recommendation
using KnnItemBasedRecommender.  My data set isn't very large at
approximately 30k preferences over 10k items.  When running
a AverageAbsoluteDifferenceRecommenderEvaluator evaluation on a 0.9
training set the result is ~0.80 (on a preference scale of 1-5).  When
tuning that training set down to only 0.1 the mean difference is closer to

I assume that this number is actually lower because there are less
recommendations that can actually be made.  Meaning that with the smaller
training set there isn't enough similarity to make recommendations, and so
those that it does make are more accurate.  So the question for me becomes,
what does the evaluation look like when only providing recommendations for
items with more than x declared preferences?  I'm wondering what the best
way to determine this.  Should I create a new recommender that only will
return items with x or more preferences (maybe using IDRescorer?) or should
I create a new evaulator to do something similar?  Is there a native method
to accomplish this that I've missed?  Is my hypothesis just likely wrong?

Appreciate the feedback.


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