An answer for this question: [Example] Files for Block Structure

There is a quick explanation in the README doc here: libFM1.42 Manual

Quick explanation is case you don’t want to read this whole blog post.

I’ll take back the toy dataset I used in this previous blog post. Look at it to get the features meaning.

train.libfm

5 0:1 2:1 6:1 9:12.5
5 0:1 3:1 6:1 9:20
4 0:1 4:1 6:1 9:78
1 1:1 2:1 8:1 9:12.5
1 1:1 3:1 8:1 9:20

and test.libfm

0 1:1 4:1 8:1 9:78
0 0:1 5:1 6:1

And I’ll merge them, so it will be easier for the whole process

dataset.libfm

5 0:1 2:1 6:1 9:12.5
5 0:1 3:1 6:1 9:20
4 0:1 4:1 6:1 9:78
1 1:1 2:1 8:1 9:12.5
1 1:1 3:1 8:1 9:20
0 1:1 4:1 8:1 9:78
0 0:1 5:1 6:1

So if we wanted to use block structure.

We will have those 5 files first:

• rel_user.libfm (features 0,1 and 6-8 are users features)

0 0:1 6:1
0 1:1 8:1

but in fact you can avoid to have feature_id_number broken like that (0-1, 6-8), we can recompress it, so (0-1 -> 0-1 and 6-8 -> 2-4)

0 0:1 2:1
0 1:1 4:1

• rel_product.libfm (features 2-5 and 9 are products features) Same thing we can compress from:

0 2:1 9:12.5
0 3:1 9:20 
0 4:1 9:78 
0 5:1

to

0 0:1 4:12.5
0 1:1 4:20
0 2:1 4:78
0 3:1

• rel_user.train (which is now the mapping, the first 3 lines correspond to the first line of rel_user.libfm | /!\ we are using a 0 indexing)

0
0
0
1
1

• rel_product.train (which is now the mapping)

0
1
2
0
1

• file y.train which contains the ratings only

5
5
4
1
1

Almost done…

Now you need to create the .x and .xt files for the user block and the product block. For this you need the script available with libFM in /bin/ after you compile them.

./bin/convert –ifile rel_user.libfm –ofilex rel_user.x –ofiley rel_user.y

you are forced to used the flag –ofiley even if rel_user.y will never be used. You can delete it every time.

and then

./bin/transpose –ifile rel_user.x –ofile rel_user.xt

Now you can do the same thing for the test set, for test because we merge the train and test dataset at the beginning, we only need to generate rel_user.test, rel_product.test and y.test

At this point, you will have a lot of files: (rel_user.train, rel_user.test, rel_user.x, rel_user.xt, rel_product.train, rel_product.test, rel_product.x, rel_produt.xt, y.train, y.test)

And run the whole thing:

./bin/libFM -task r -train y.train -test y.test –relation rel_user,rel_product -out output

It’s a bit overkill for this problem but I hope you get the point.

Now a real example

For this example, I’ll use the ml-1m.zip MovieLens dataset that you can get from here (1 million ratings)

ratings.dat (sample) / Format: UserID::MovieID::Rating::Timestamp

1::1193::5::978300760
1::661::3::978302109
1::914::3::978301968
1::3408::4::978300275
1::2355::5::978824291

movies.dat (sample) / Format: MovieID::Title::Genres

1::Toy Story (1995)::Animation|Children’s|Comedy
2::Jumanji (1995)::Adventure|Children’s|Fantasy
3::Grumpier Old Men (1995)::Comedy|Romance
4::Waiting to Exhale (1995)::Comedy|Drama

users.dat (sample) / Format: UserID::Gender::Age::Occupation::Zip-code

1::F::1::10::48067
2::M::56::16::70072
3::M::25::15::55117
4::M::45::7::02460
5::M::25::20::55455

I’ll create 3 different models.

1. Easiest libFM files to train without block. I’ll use those features: UserID, MovieID
2. Regular libFM files to train without block. I’ll use those features: UserID, MovieID, Gender, Age, Occupation, Genre (of movie)
3. libFM files to train with block. I’ll also use those features: UserID, MovieID, Gender, Age, Occupation, Genre (of movie)

Model 1 and 2 can be created using the following code:

So you end up with a file model1.libfm and model2.libfm. Just need to split each of those files in two to create a training et test set file that I’ll call train_m1.libfm, test_m1.libfm (same thing for model2, train_m2.libfm, test_m2.libfm)

Then you just run libFM like this:

./libFM -train train_m1.libfm -test test_m1.libfm -task r -iter 20 -method mcmc -dim ‘1,1,8’ -output output_m1

But I guess you already know how to do those.

Now the interesting part, using blocks.

[TODO]