Simply type "make" in the terminal line or follow the commands in the Makefile.
pl2m_train is used to train models based on train/test files and user/item feature matrix files, which needs 6 parameters:
[usage] <configure-file> <user-feature-matrix> <item-feature-matrix> <train> <test> <model-output-prefix>
pl2m_infer is used to make predictions based on trained models, which needs 3 parameters:
[usage] <test> <particular-model> <prediction-output-file>
For more details, please check the example.
Please check the "examples/pcf.conf" for details.
Spare Matrix for user features and item features.
For each line, first come with the id of user/item. Then followed by the number of non-zero features. Features are described by their indices and values, separated by a colon. Here is an example for user 123, who have 5 non-zero features in total.
123 5 123:1 1:0.5 10:0.5 11:0.5 20:0.5
For more details, please check the example.
Classical 3/4 columns, where weight of this instance is optional (1 default). However, in one train/test files, the columns in each line should be same.
user item rate [weight]
For more details, please check the example.
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run the buildFeatMat.py in the folder "data".
python buildFeatMat.pywhich will generate 4 four files in the folder "data", which are the train/test files and user/item feature matrix files.
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get into the folder "example", create a folder "models".
mkdir models -
run the predefined script.
./run.sh
which includes both training and testing procedures.
If you are using this toolkit for some research, please cite the following papers.
[1] Shang, J., Chen, T., Li, H., Lu, Z., & Yu, Y. (2014). A Parallel and Efficient Algorithm for Learning to Match. In Data Mining (ICDM), 2014 IEEE 14th International Conference. IEEE.
There is also a long version of this paper available on arXiv.
http://arxiv.org/abs/1410.6414