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From Matt Mitchell <>
Subject Re: Generating similarity file(s) for item recommender?
Date Tue, 03 Jul 2012 12:15:23 GMT
Thanks Mridul, I'll try this out. Does getItemIDs return every item id
from the file in your example?

This kind of leads me to another, related question... I want to have
my recommender engine recommend items to a user, but the items should
be from a known set of item ids. For example, if a user is doing a
search for "gaming system", I only want recommendations for "gaming
system" items. I was thinking I could feed the recommendation engine a
set of item IDs that are known to be "gaming systems" as a candidate
set *when executing that actual recommendation*. Does this make sense?
If so, do you know how I can do this? I basically want to constrain
the recommendations to a set of known item IDs at recommendation time.

Thanks again!

- Matt

On Tue, Jul 3, 2012 at 8:01 AM, Mridul Kapoor <> wrote:
>> I'm thinking the session ID (in the cookie) would be used as the user ID.
>> The events
>> are tied to product IDs, so these would be used in generating the
>> preferences.
> I guess if you consider product-preference on a per session-basis (i.e.
> only items for which a user expresses preference for, in a single session,
> are similar to each other, in some way or the other). This way, you would
> be considering the session-ids as dummy user-ids, which I think should be
> good.
> I'd like to eventually run this on Hadoop, but it'd also be nice to know if
>> there is a way to do this locally, while developing the app, maybe using a
>> smaller
>> dataset?
> Yes just writing a small offline recommender (made to run on a local
> machine) should do; you could take a subset of the data, use a
> FileDataModel, then do something like
> LongPrimitiveIterator itemIDs = dataModel.getItemIDs();
> and iterate over these; getting _n_ recommended items for each, storing
> them somewhere (and maybe use this evaluating the recommender somehow)
> Best,
> Mridul

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