From: Grant Ingersoll <[EMAIL PROTECTED]>
To: [email protected]
Sent: Monday, September 29, 2008 9:43:58 AM
Subject: Re: Recommending when working with binary data sets
Not sure I know the answer in terms of Taste, but did a little bit of
digging (mind you, I'm no CF expert, but I'm learning thanks to Taste
and Sean).
At any rate, came across:
Started at Wikipedia's page:
http://en.wikipedia.org/wiki/Collaborative_filtering
which lead to http://en.wikipedia.org/wiki/Slope_One, which then has
an interesting comment about Amazon's item-item approach, which, via
Google Scholar leads to:
http://dsonline.computer.org/portal/site/dsonline/menuitem.9ed3d9924aeb0dcd82ccc6716bbe36ec/index.jsp?&pName=dso_level1&path=dsonline/2003_Archives/0301/d&file=wp1lind.xml&xsl=article.xsl&;jsessionid=LghY1grHgYJpBTLpWjX5NtvQwhH1Bkv9rpfXT4VnpVtDNVpfZ8n0!-1404507079
In particular, see the "How it Works" section. Essentially, it
describes how they build the item to item similarity matrix, which I
believe is also what you need to do.
HTH,
Grant
On Sep 26, 2008, at 1:52 PM, Otis Gospodnetic wrote:
Hi,
I've been reading the chapter on recommendations in Programming
Collective Intelligence and looking at Taste. The examples in PCI
all assume scenarios where items to recommend have been rated by
users on some scale. I understand how items can be recommended to
users using item-based filtering and user-item ratings and why this
is preferred over user-based filtering when the number of users is
larger than the number of items.
But what if all I've got is item-item similarity (content-based) and
there are no user-item ratings? Say I have a situation where people
simply either consume content (e.g. read an article, watch a
video...) or not consume it (don't read an article, don't watch the
video...). In other words, I really have only yes/no or 1/0 or
seen/
not seen type "rating".
I can't really use Euclidean distance or Pearson correlation
coefficient, can I?
What do people use in such scenarios? Would it make sense to use
http://en.wikipedia.org/wiki/Jaccard_index
for such cases?
... Ah, I do see javadoc in TanimotoCoefficientSimilarity saying
exactly that, good.
But then my question is:
Doesn't the use of Jaccard/Tanimoto mean going back to the expensive
user-user similarity computation?
That is, if I need to recommend items for user U1 don't I need to:
1) have user-user similarity pre-computed (and recomputed
periodically)
2) find top N users U{2,3,4,...N} who are the most similar to U1
3) then for these top N most similar users find their "seen" items
that U1 has not seen (possibly limit this to only recently seen
items)
4) select top N items from 3) and recommend those to U1.
If so, then 1) is again expensive.
And what how would one go about selecting top N items from the list
in this case other than ordering them by user-user similarity?
Of course, something is telling me I'm demonstrating that I don't
yet have the full grasp of item-based filtering. I hope that's the
case! :)
Thanks,
Otis
