Revisiting the Tag Relevance Prediction Problem

Introduction

This repository contains data pipeline to run experiments described in [Vig et al., 2012] and [Kotkov et al., 2021]. The pipeline takes item and tag data as input and produces scores that indicate degrees, to which tags apply to items. The pipeline consists of the following steps:

• Raw data transformation – converting raw data to a suitable format with ./data/make_interim_pickle_files.py
• Feature generation – extracting features from the converted data with ./features/build_features.py and ./models/r/build_features.R
• Data split – preparing data for evaluation with ./data/make_10folds.py
• Prediction – predicting scores based on extracted features with ./models/r/run_tenfolds.R and ./models/run_tenfolds.py
• Evaluation – measuring performance of algorithms with ./models/calcl_mae_for_folds.py

Datasets

• The Tag Genome dataset for movies: https://grouplens.org/datasets/movielens/tag-genome-2021/
• The Tag Genome dataset for books: https://grouplens.org/datasets/book-genome

Usage License

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 License. If you are using these code and/or datasets, please cite the following papers:

• [Kotkov et al., 2021] Kotkov, D., Maslov, A., and Neovius, M. (2021). Revisiting the tag relevance prediction problem. In Proceedings of the 44th International ACM SIGIR conference on Research and Development in Information Retrieval.
• [Vig et al., 2012] Vig, J., Sen, S., and Riedl, J. (2012). The tag genome: Encoding community knowledge to support novel interaction. ACM Trans. Interact. Intell. Syst., 2(3):13:1–13:44.

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Set up

python -m venv venv
source venv/bin/activate
pip install -e .

Create .env file with the path to the project directory

# Environment variables go here, can be read by `python-dotenv` package:
#
#   `src/script.py`
#   ----------------------------------------------------------------
#    import dotenv
#
#    project_dir = os.path.join(os.path.dirname(__file__), os.pardir)
#    dotenv_path = os.path.join(project_dir, '.env')
#    dotenv.load_dotenv(dotenv_path)
#   ----------------------------------------------------------------
#
# DO NOT ADD THIS FILE TO VERSION CONTROL!
PROJECT_DIR="/home/u/project_dir"
DIR_DATA_RAW="/home/u/../data/raw"

Run

1. src/data/make_interim_pickle_files.py
2. src/features/build_features.py
3. Run models (R/PyTorch)

or, instead 1) and 2)

cd src/
chmod +x run.sh 
./run.sh

Input data format

data/raw_example

Data flow

mainanswers.txt -> build_features -> Input files for R -> dump for r and Pytorch models

Generating Tag Genome scores

To generate Tag Genome scores, run the following scripts:

1. src/run.sh
2. src/models/r/generate_features_for_scores.R
3. src/models/r/generate_scores.R
4. src/models/generate_scores.py

Scripts:

• src/models/r/generate_features_for_scores.R – generates features for prediction algorithms. The script requires the file data/processed/movie_ids.csv, which contains ids of items to included in the Tag Genome scores. The file contains ids in the csv format in the field: movie_id.
• src/models/r/generate_scores.R – generates scores with the method presented in [Vig et al., 2012]
• src/models/generate_scores.py – generates scores with TagDL [Kotkov et al., 2021]

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── predictions    <- Preditions of models.
│   ├── pickle_files   <- Serrialized objects.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The feature data sets for modeling.
│   ├── raw            <- The original, immutable data dump (item data).
│   └── raw_example    <- Example of the raw data
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── run.sh         <- The script for running the project
│   │
│   ├── data           <- Scripts to generate intermediate data
│   │   └── make_interim_pickle_files.py
│   │
│   ├── features       <- Scripts to turn intermediate data into features for modeling
│   │   └── build_features.py
│   │
│   └── models         <- Scripts to train models and make predictions (+ logistic regression for calculating the tag_prob feature)
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Acknowledgements

We would like to thank GroupLens for providing us with the dataset and code for the regression algorithm [Vig et al., 2012]. We would also like to thank organizations that supported publication of this dataset: the Academy of Finland, grant #309495 (the LibDat project) and the Academy of Finland Flagship programme: Finnish Center for Artificial Intelligence FCAI.

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