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From Koobas <>
Subject GenericUserBasedRecommender vs GenericItemBasedRecommender
Date Thu, 21 Feb 2013 14:37:26 GMT
In the GenericUserBasedRecommender the concept of a neighborhood seems to
be fundamental.
I.e., it is a classic implementation of the kNN algorithm.

But it is not the case with the GenericItemBasedRecommender.
I understand that the two approaches are not meant to be completely
but still, wouldn't it make sense, from the performance perspective, to
compute items' neighborhoods first,
and then use them to compute recommendations?

If kNN was run on items first, then every item-item similarity would be
computed once.
It looks like in the GenericItemBasedRecommender each item-item similarity
will be computed multiple times.
(How much, depends on the data, but still.)

I am wondering if anybody has any thoughts on the validity of doing
item-item kNN in the context of:
1) performance,
2) quality of recommendations.

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