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From Jake Mannix <>
Subject Re: Taste-GenericItemBasedRecommender
Date Sun, 13 Dec 2009 03:24:02 GMT
On Sat, Dec 12, 2009 at 3:16 PM, Sean Owen <> wrote:

Recommendations are computed for one user at a time, by multiplying
the co-occurrence matrix by the user preference vector. And then yes
it's one big job invoking computation for all users.

I'm running this all one one machine (my laptop) so it's kind of
serialized anyway. yes it was 10 seconds to compute all recs for one
user; it's a couple secs now with some more work. That's still rough
but not awful.

 So when doing a big batch of a thousand users, say, you're saying it's
taking your laptop 3 hours to do this using the Hadoop-based code (in
pseudo-distributed mode)?

All of it is on Hadoop here. It's pretty simple -- make the user

vectors, make the co-occurrence matrix (all that is quite fast), then
multiply the two to make recommendations.

 You do the co-occurrence matrix (for item-by-item, right?) on Hadoop too,
and that part is really fast, but computing the recommendations is very
slow?  By what orders of magnitude, for the whole set?

What are the scales you are testing with, in terms of total number of users,
items, and ratings?


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