Question - how to get final recommendations for each user?

[eddiesun]

I ran wals and got 2 output files, one is the user matrix and other is the item matrix.
My question is, is there some tool that can multiply the two matrices together to get the final user-item matrix, which can be used to get recommendations for each user?

I have the following 2 factor files as the output of wals

// user factors file 
user1 factor_1 factor_2 factor_3 ...
user2 factor_1 factor_2 factor_3 ... 
user3 factor_1 factor_2 factor_3 ...
...

// item factors file 
item1 factor_1 factor_2 factor_3 ...
item2 factor_1 factor_2 factor_3 ... 
item3 factor_1 factor_2 factor_3 ...
...

What I want is something like this

user1 item3|score item1|score item5|score ...
user2 item9|score item2|score item8|score ...
...

For each user, item recommendations are sorted by their scores
If I have the output above, I can recommend item3, item1, item5 to user1 and item9, item2 item8 to user2, etc.

Is there a tool to do this or I have to write something to multiply the 2 matrices and do filtering/sorting myself?

Thanks,
Eddie

[albietz]

Hi Eddie,
We did not initially plan to provide this functionality in the library, that said it should indeed be easy to just do a matrix multiply in numpy/pandas/other if your dataset is not too big (or just for few users at a time).

[eddiesun]

Hi Alberto,
Thank you for the clarification. Our dataset is big, so I will use/write something else to process it.
qmf is a good tool. Thanks again for your contribution.