Hey, 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 <[email protected]> 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 >
