FranKGraphBench: Knowledge Graph Aware Recommender Systems Framework for Benchmarking

The FranKGraphBench is a framework to allow KG Aware RSs to be benchmarked in a reproducible and easy to implement manner. It was first created on Google Summer of Code 2023 for Data Integration between DBpedia and some standard RS datasets in a reproducible framework.

Check the docs for more information.

• This repository was first created for Data Integration between DBpedia and some standard Recommender Systems datasets and a framework for reproducible experiments. For more info, check the project proposal and the project progress with weekly (as possible) updates.

Data Integration Usage

pip

We recommend using a python 3.8 virtual environment

pip install pybind11
pip install frankgraphbench

Install the full dataset using bash scripts located at datasets/:

cd datasets
bash ml-100k.sh # Downloaded at `datasets/ml-100k` folder
bash ml-1m.sh   # Downloaded at `datasets/ml-1m` folder

Usage

data_integration [-h] -d DATASET -i INPUT_PATH -o OUTPUT_PATH [-ci] [-cu] [-cr] [-cs] [-map] [-w]

Arguments:

• -h: Shows the help message.
• -d: Name of a supported dataset. It will be the same name of the folder created by the bash script provided for the dataset. For now, check data_integration/dataset2class.py to see the supported ones.
• -i: Input path where the full dataset is placed.
• -o: Output path where the integrated dataset will be placed.
• -ci: Use this flag if you want to convert item data.
• -cu: Use this flag if you want to convert user data.
• -cr: Use this flag if you want to convert rating data.
• -cs: Use this flag if you want to convert social link data.
• -map: Use this flag if you want to map dataset items with DBpedia. At least the item data should be already converted.
• -w: Choose the number of workers(threads) to be used for parallel queries.

Usage Example:

data_integration -d 'ml-100k' -i 'datasets/ml-100k' -o 'datasets/ml-100k/processed' \
    -ci -cu -cr -map -w 8

source

Install the required packages using python virtualenv, using:

python3 -m venv venv_data_integration/
source venv_data_integration/bin/activate
pip3 install -r requirements_data_integration.txt 

Install the full dataset using bash scripts located at datasets/:

cd datasets
bash ml-100k.sh # Downloaded at `datasets/ml-100k` folder
bash ml-1m.sh   # Downloaded at `datasets/ml-1m` folder

Usage

python3 data_integration.py [-h] -d DATASET -i INPUT_PATH -o OUTPUT_PATH [-ci] [-cu] [-cr] [-cs] [-map] [-w]

Arguments:

• -h: Shows the help message.
• -d: Name of a supported dataset. It will be the same name of the folder created by the bash script provided for the dataset. For now, check data_integration/dataset2class.py to see the supported ones.
• -i: Input path where the full dataset is placed.
• -o: Output path where the integrated dataset will be placed.
• -ci: Use this flag if you want to convert item data.
• -cu: Use this flag if you want to convert user data.
• -cr: Use this flag if you want to convert rating data.
• -cs: Use this flag if you want to convert social link data.
• -map: Use this flag if you want to map dataset items with DBpedia. At least the item data should be already converted.
• -w: Choose the number of workers(threads) to be used for parallel queries.

Usage Example:

python3 data_integration.py -d 'ml-100k' -i 'datasets/ml-100k' -o 'datasets/ml-100k/processed' \
    -ci -cu -cr -map -w 8

Check Makefile for more examples.

Supported datasets

Dataset	#items matched	#items
MovieLens-100k	1462	1681
MovieLens-1M	3356	3883
LastFM-hetrec-2011	11815	17632
Douban-Movie-Short-Comments-Dataset	---	28
Yelp-Dataset	---	150348
Amazon-Video-Games-5	---	21106

Framework for reproducible experiments usage

pip

We recommend using a python 3.8 virtual environment

pip install pybind11
pip install frankgraphbench

Usage

framework -c 'config_files/test.yml'

Arguments:

• -c: Experiment configuration file path.

The experiment config file should be a .yaml file like this:

experiment:
  dataset: 
    name: ml-100k
    item:
      path: datasets/ml-100k/processed/item.csv 
      extra_features: [movie_year, movie_title] 
    user:
      path: datasets/ml-100k/processed/user.csv 
      extra_features: [gender, occupation] 
    ratings: 
      path: datasets/ml-100k/processed/rating.csv 
      timestamp: True
    enrich:
      map_path: datasets/ml-100k/processed/map.csv
      enrich_path: datasets/ml-100k/processed/enriched.csv
      remove_unmatched: False
      properties:
        - type: subject
          grouped: True
          sep: "::"
        - type: director
          grouped: True
          sep: "::"

  preprocess:
    - method: filter_kcore
      parameters:
        k: 20
        iterations: 1
        target: user

  split:
    seed: 42
    test:
      method: k_fold
      k: 2
      level: 'user'


  models:
    - name: deepwalk_based
      config:
        save_weights: True
      parameters:
        walk_len: 10
        p: 1.0
        q: 1.0
        n_walks: 50
        embedding_size: 64
        epochs: 1
  
  evaluation:
    k: 5
    relevance_threshold: 3
    metrics: [MAP, nDCG]

  report:
    file: 'experiment_results/ml100k_enriched/run1.csv'

See the config_files/ directory for more examples.

source

Install the require packages using python virtualenv, using:

python3 -m venv venv_framework/
source venv_framework/bin/activate
pip3 install -r requirements_framework.txt 

Usage

python3 framework.py -c 'config_files/test.yml'

Arguments:

• -c: Experiment configuration file path.

The experiment config file should be a .yaml file like this:

experiment:
  dataset: 
    name: ml-100k
    item:
      path: datasets/ml-100k/processed/item.csv 
      extra_features: [movie_year, movie_title] 
    user:
      path: datasets/ml-100k/processed/user.csv 
      extra_features: [gender, occupation] 
    ratings: 
      path: datasets/ml-100k/processed/rating.csv 
      timestamp: True
    enrich:
      map_path: datasets/ml-100k/processed/map.csv
      enrich_path: datasets/ml-100k/processed/enriched.csv
      remove_unmatched: False
      properties:
        - type: subject
          grouped: True
          sep: "::"
        - type: director
          grouped: True
          sep: "::"

  preprocess:
    - method: filter_kcore
      parameters:
        k: 20
        iterations: 1
        target: user

  split:
    seed: 42
    test:
      method: k_fold
      k: 2
      level: 'user'


  models:
    - name: deepwalk_based
      config:
        save_weights: True
      parameters:
        walk_len: 10
        p: 1.0
        q: 1.0
        n_walks: 50
        embedding_size: 64
        epochs: 1
  
  evaluation:
    k: 5
    relevance_threshold: 3
    metrics: [MAP, nDCG]

  report:
    file: 'experiment_results/ml100k_enriched/run1.csv'

See the config_files/ directory for more examples.