You can also use the new MultithreadedBatchItemSimilarities class to
efficiently precompute item similarities on a single machine without
having to go to MapReduce.
On 12.04.2013 00:54, Pat Ferrel wrote:
> Do you not have a user ID? No matter (though if you do I'd use it) you can
> use the item I
Do you not have a user ID? No matter (though if you do I'd use it) you can use
the item ID as a surrogate for a user ID in the recommender. And there will be
no filtering if you ask for recommender.mostSimilarItems(long itemID, int
howMany), which has no user ID in the call and so will not filte
You can actually create a "user" #6 for your new order. Or you can use
the "anonymous user" function of the library, although it's hacky.
We may be mixing up terms here. "DataModel" is a class that has
nothing to do with Hadoop. Hadoop in turn has no part in real-time
anything, like recommending t
As in the example data 'intro.csv' in the MIA it has users 1-5 so if I ask
for recommendations for user 1 then this works but if I ask for
recommendations for user 6 (a new user yet to be added to the data model)
then I get no recommendations ... so if I substitute users for orders then
again I wil
Actually, making this user based is a really good thing because you get
recommendations from one session to the next. These may be much more
valuable for cross-sell than things in the same order.
On Thu, Apr 11, 2013 at 12:50 PM, Sean Owen wrote:
> You can try treating your orders as the 'user
You can try treating your orders as the 'users'. Then just compute
item-item similarities per usual.
On Thu, Apr 11, 2013 at 7:59 PM, Billy wrote:
> Thanks for replying,
>
>
> I don't have users, well I do :-) but in this case it should not influence
> the recommendations
>
> ,
> these need to be
Use ItemSimilarityJob instead of RowSimilarityJob, its the easy-to-use
wrapper around that :)
On 11.04.2013 19:28, Sean Owen wrote:
> This sounds like just a most-similar-items problem. That's good news
> because that's simpler. The only question is how you want to compute
> item-item similarities
Or you may want to look at recording purchases by user ID. Then use the
standard recommender to train on (userID, itemsID, boolean). Then query the
trained recommender thus: recommender.mostSimilarItems(long itemID, int
howMany) This does what you want but uses more data than just what items wer
This sounds like just a most-similar-items problem. That's good news
because that's simpler. The only question is how you want to compute
item-item similarities. That could be based on user-item interactions.
If you're on Hadoop, try the RowSimilarityJob (where you will need
rows to be items, colum
I am very new to Mahout and currently just ready up to chapter 5 of 'MIA'
but after reading about the various User centric and Item centric
recommenders they all seem to still need a userId so still unsure if Mahout
can help with a fairly common recommendation.
My requirement is to produce 'n' ite
10 matches
Mail list logo
