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From Johannes Schulte <>
Subject Re: Item recommendation w/o users or preferences
Date Tue, 14 Jan 2014 07:46:42 GMT

since you are already using basket analysis terms like support, confidence
and lift it might be easier for you to think of the llr score as a "better
lift" since it automatically puts a penalty on seldom items (you usually
use support in classic mba for that).

So, you would use the same 4 counts you use for lift calculation
(cooccurrence,2*marginals, totals), and you do this for every combination
of items that occur in a basket

On Sat, Jan 11, 2014 at 10:38 PM, Tim Smith <> wrote:

> > Is it about how to arrange your data to use this computation?  The
> > references below might help with that.
> Yes, I read and tried the recommendation examples from MIA and there is a
> mention of item to item similarity, but I am not sure what form the file
> should take.  The examples are along the lines of  userid,itemid,value
> In section 6.2 of MIA we are multiplying the Co-occur matrix X User
> preferences = Recommendations (top of page 97), so if I do not have
> preferences should
>  I just default them all to the same value?  Taken together with your
> previous comments, is this how I should be preparing my data?
> Raw Sample Data (format: Transaction|Item)
> 123|Sun Glasses
> 124|Sun Glasses
> 124|Sun Glass Case
> 125|Sun Glass Case
> 126|Sun Glasses
> 126|Glass Repair Kit
> 127|Glass Repair Kit
> Are you suggesting that I just simply use (format:  userid|item|value)
> 123|Sun Glasses|1
> 124|Sun Glasses|1
> 124|Sun Glass Case|1
> 125|Sun Glass Case|1
> 126|Sun Glasses|1
> 126|Glass Repair Kit|1
> 127|Glass Repair Kit|1
> > Is it regarding the specifics of how you do the computation?  I can help
> > with that, but would need a pointer to the difficulty.
> Not quite yet.  I am working through the intuition first, I'll fight
> through the math once, if ever, the fog clears

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