recommendation_systems_fairness

KO | EN

gitlite — search

#javascript #python #hacktoberfest #typescript #go #golang #deep-learning #linux #docker #nodejs #java #windows #pytorch #open-source

recommendation_systems_fairness

(★ 15)

This repository accompaines the paper "Investigating Gender Fairness of Recommendation Algorithms in the Music Domain"

recommendation_systems_fairness Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Investiating Gender Fairness of Recommendation Algorithms in the Music Domain

This repository accompanies the paper "`Investigating Gender Fairness of Recommendation Algorithms in the Music Domain`" by Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, and Markus Schedl.

## Installation

Create the environment with

~~~
conda env create -f gender_fair.yml
conda activate gender_fair
~~~

and then setup the project with
~~~
python3 setup.py develop
~~~

## Setup the data
Data is available at `http://www.cp.jku.at/datasets/LFM-2b/`

To sample the items as done in the paper, please check `item_sampler.ipynb` in `notebooks` folder.

Data paths must be set in the `conf.py` file. In particular:
- `DATA_PATH`: points to a .tsv file with columns [`user_id`,`track_id`,`play_count`].
- `DEMO_PATH`: points to a .tsv file with columns [`user_name`,`country`,`age`,`gender`,`timestamp`].
- `TRACKS_PATH`: points to a .tsv file with columns [`track_artist`,`track_name`].
- `DOWN_DATA_PATH`: similar to `DATA_PATH` but contains the balanced dataset (optional).
- `DOWN_DEMO_PATH`: similar to `DEMO_PATH` but contains the balanced dataset (optional).
- `OUT_DIR`: where the processed datasets (e.g train/val/test data for fold 0).
## Run an experiment (Train + Validation)
`algorithms` contains all the algorithms used in the project. Move to the folder of the algorithm of your choice (e.g. `slim`)
~~~
cd algorithms/slim
~~~
and with the active env, run the experiment
~~~
python3 main_slim.py --experiment_type <experiment_type>
~~~
`experiment_type` can be:
- `standard`: original data
- `up_sample`: debiasing method where the minority group in the training data is upsampled (as specified in the paper)
- `down_sample`: balanced data is used instead where users from the majority groups have been discarded

for `vae` there is also the parameter `gpu` which specifies which gpu to use.
## Check the experiment status
Experiment status and results will be saved in the `res` folder (created when the first experiment is run).
 
To check the status of your experiment, move in the folder of the algorithm you are running (e.g. `slim`):
~~~
cd res/slim/<experiment_type>-<timestamp_start_experiment>/val
~~~

For a broad overview of the experiments, you can just use `tensorboard` in the folder.
~~~
tensorboard --logdir=./
~~~
You can also jump to a specific test fold by selecting one of the folders there (numbered from 0 to 4 for 5-folds cross validation).
E.g. monitor how the experiment is going on fold 0.
~~~
cd 0
tensorboard --logdir=./
~~~

## Running evaluation
Experiment status and results will be saved in the `res` folder (created when the first experiment is run).

To run the evaluation when an experiment has ended, move in the folder of the algorithm you run (e.g. `slim`)
~~~
cd algorithms/slim
~~~
and the run the evaluation with:
~~~
python3 eval_slim.py --experiment_type <experiment_type>
~~~
where `experiment_type` parameter is defined as above in "Check the experiment status".

**NOTE:** Evaluation requires loading the trained model (except for `pop`). The code will look in the `res` folder for the best configuration automatically.
It is needed to update the path to the trained model by providing the `timestamp_start_experiment` in the evaluation code.
E.g. for `eval_slim.py` you can find this at the top of the code:
~~~python
best_configs = {
    'standard': '2020-11-24 19:43:47.801230',
    'up_sample': '2020-11-24 19:44:32.188924',
    'down_sample': '2020-11-24 19:45:15.744244'
}
~~~
Set the timestamp of your experiment in the dictionary (in the correct `experiment_type` field) for running the evaluation.

E.G. `slim` is started at `2020-11-24 19:43:47.801230 with
~~~
python3 main_slim.py --experiment_type standard
~~~
in `res` there will be a folder `slim/standard-2020-11-24 19:43:47.801230`.
After the training and validation, place `2020-11-24 19:43:47.801230` in the filed `standard` of the `best_configs` dictionary in `eval_slim.py`

## Results and best configurations
Experiment status and results will be saved in the `res` folder (created when the first experiment is run).

### Results
To check the results of your experiment, move in the folder of your experiment (e.g. `slim`):
~~~
cd res/slim/<experiment_type>-<timestamp_start_experiment>/test
~~~
For a broad overview of the experiments, you can just use `tensorboard` in the folder.
~~~
tensorboard --logdir=./
~~~

The results are saved separately for each fold in the relative folder.
In each fold folder you will find:
~~~
full_metrics.pkl
full_raw_metrics.pkl
~~~
Both files contain a dictionary where the key is the evaluated metric function and the value is the result of the metric function on the test data.
- `full_raw_metrics.pkl` contains vectors as values of the dictionary (results are **not** aggregated).
- `full_metrics.pkl` contains floats as values of the dictionary (results are aggregated with mean)

### Best configurations
The best configuration for each algorithm and for each fold is located in:
~~~
cd res/slim/<experiment_type>-<timestamp_start_experiment>/val/<fold-number>/best_config.pkl
~~~

### Significance Testing
The significant testing is located in `signigicance_testing` folder. Code is in R and Jupyter notebook.
For instructions, refer to the README in the folder.
~~~
cd significance_testing
~~~

recommendation_systems_fairness

recommendation_systems_fairness Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

recommendation_systems_fairness

recommendation_systems_fairness Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README