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From jamborta <>
Subject Re: item-based recommendation neighbourhood size
Date Sat, 20 Feb 2010 11:22:50 GMT

but as far as I understand your implementation you take user1 and then get
all the items
that the user hasn't rated (getAllOtherItems()) and generate recommendation
for each of these items. therefore, you have user1 item1, user1 item2, etc
as input. so the neighbourhood can be restricted for each of these items.


If you are making recommendations, then there is no item1 as input.
You're only given user1. This is true in user-based or item-based

You are right that if we just wanted to predict one rating, you would
have user1 and item1 as input. All of the existing recommender
implementations actually can do this through the estimatePreference()

None work by computing a neighborhood of items, since that's not
suitable to make recommendations. However the item-based recommender
in the framework can tell you the items most similar to a given item.
You could use that on your own to perform the computation you are
thinking of.

You might run into the following issue: in a sparse data set, user1
might not have rated anything in the immediate neighborhood of item1.


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