Pramit Vamsi 2010-08-29, 17:25
Sean Owen 2010-08-29, 17:39
Ted Dunning 2010-08-29, 18:26
Sean Owen 2010-08-29, 18:37
Pramit Vamsi 2010-08-30, 14:47
Ted Dunning 2010-08-30, 16:44
Jake Mannix 2010-08-30, 17:34
Sean Owen 2010-08-30, 15:52
I would take the user-search "matrix" and create one DataModel from
it. Define a LogLikelihoodSimilarity object on top of that. That's
your user-user similarity measure.
Then the user-purchase "matrix" forms the basis of another DataModel
that is actually plugged into a GenericUserBasedRecommender with the
similarity measure above (which actually drives off different data).
Off the top of my head, that ought to work out.
The second situation might just be really simple. If you have view and
purchase data, simply count up and find which purchase was most
frequent among all purchases that followed from a view of the current
item. That's simple, perhaps oversimplified for your context.
I can think of ways to construe this as a CF problem but I think it
just adds complication with no value. It's not really CF.
On Mon, Aug 30, 2010 at 3:47 PM, Pramit Vamsi <[EMAIL PROTECTED]> wrote:
> I have some understanding now. So given 2 matrices user * (page view/search
> term) and user * (purchased item), how do you connect these 2 matrices
> given that I can define the user or item sim methods?
> Also, can the second use case can be solved with CF or association mining is
> On Mon, Aug 30, 2010 at 12:07 AM, Sean Owen <[EMAIL PROTECTED]> wrote:
>> Yes, this is a simpler problem. You just want to find which items are
>> most similar to a given item, for some definition of 'similar'.
>> GenericItemBasedRecommender has a mostSimilarItems() method that just
>> saves you the trouble of computing this by hand, and any
>> ItemSimiliarity function you like can be used.
>> On Sun, Aug 29, 2010 at 7:26 PM, Ted Dunning <[EMAIL PROTECTED]>
>> > These are examples of what I call cross-recommendation where you have
>> user x
>> > item1 and user x item2 data and you
>> > want item1 => item2 recommendations.
>> > All of the standard techniques apply (user-based, LLR cooccurrence, SVD,
>> > latent factor models), but you have to rejigger things here
>> > and there.
>> > Sean, can Mahout's recommendation system do this cross recommendation?
Chris Bates 2010-08-30, 15:20