Overview

AUTO_DataGenCARS+ is a complete Python-based synthetic dataset generator for the valuation of Traditional Recommendation Systems (RS) and Context-Aware Recommendation Systems (CARS).

The generator presents features such as:

• A flexible definition of user profiles, use, item and context schemas.
• A realistic generation of ratings (implicit and explicit) and attributes of items.
• The possibility to mix real and synthetic datasets.
• Functionalities to analyze existing datasets as a basis for synthetic data generation.
• Support for the automatic mapping between item schemas and Java classes.
• Analysis and evaluation of RS anc CARS with generated datasets.

It was designed with the following purposes:

• Generate a synthetic dataset:
  • Explicit ratings
• Pre-process a dataset:
  • Generate NULL values
  • Replace NULL values
  • Generate user profile (manual and automatic)
  • Replicate dataset
  • Extend dataset
  • Recalculate ratings
  • Transform attributes
• Analysis of a dataset:
  • Visualization:
    • user
    • item
    • context <optional>
    • rating
  • Evaluation:
    • RS: collaborative filtering and content-based information
    • CARS: pre-filtering, post-Filtering and contextual modeling paradigms

Demo

AUTO-DataGenCARS+ has a user-freindly demo based on Streamlit. To use it, credentials are required and must be requested by sending an email to mcrodriguez@ita.es

Requirements

The libraries used in this project with its respective versions can be seen in environment.yml.

License

Open source license: If you are creating an open source application under a license compatible with the GNU GPL license v3 you may use AUTO-DataGenCARS+ under its terms and conditions.

Reference

Please make sure to cite the paper if you use AUTO-DataGenCARS+ for your research:

@article{mc2017datagencars,
         title = {DataGenCARS: A generator of synthetic data for the evaluation of context-aware recommendation systems},
         journal = {Pervasive and Mobile Computing},                      
         note = {Special Issue IEEE International Conference on Pervasive Computing and Communications (PerCom) 2016},
         year = {2017},
         publisher = {Elsevier},
         doi = {10.1016/j.pmcj.2016.09.020},      
         volume = {38},
         number = {2},
         pages = {516-541},
         issn = {1574-1192},
         author = {María del Carmen Rodríguez-Hernández and Sergio Ilarri and Ramón Hermoso and Raquel Trillo-Lado}         
        }

@inproceedings{dexa2024,
	  author = {Marcos Caballero and María del Carmen Rodríguez-Hernández and Raúl Parada and Sergio Ilarri and Raquel Trillo-Lado and Ramón Hermoso and Óscar J. Rubio},
	  booktitle = {35th International Conference on Database and Expert Systems Applications (DEXA 2024), Naples (Italy)},
	  month = {August},
	  pages = {267--273},
	  publisher = {Springer, ISSN 0302-9743, ISSN 1611-3349 (electronic), Print ISBN 978-3-031-68308-4, Online ISBN 978-3-031-68309-1},
	  series = {Lecture Notes in Computer Science (LNCS)},
	  volume = {14910},
	  title = {An Approach for Social-Distance Preserving Location-Aware Recommender Systems: A Use Case in a Hospital Environment},
	  doi = {10.1007/978-3-031-68309-1_23},
	  year = {2024}
}

@inproceedings{momm2024,
	  author = {María del Carmen Rodríguez Hernández and Sergio Ilarri and Marcos Caballero and Raquel Trillo-Lado and Ramón Hermoso and Rafael del Hoyo Alonso},
	  booktitle = {22nd International Conference on Advances in Mobile Computing and Multimedia Intelligence (MoMM 2024), Bratislava (Slovakia)},
	  month = {December},
	  pages = {176--191},
	  title = {AUTO-DataGenCARS+: An Advanced User-Oriented Tool to Generate Data for the Evaluation of Recommender Systems},
	  year = {2024},
	  doi = {10.1007/978-3-031-78049-3_16},
	  series = {Lecture Notes in Computer Science (LNCS)},
	  publisher = {Springer, ISSN 0302-9743, ISSN 1611-3349 (electronic), Print ISBN 978-3-031-78048-6, Online ISBN 978-3-031-78049-3},
	  editor = {Pari Delir Haghighi and Solomiia Fedushko and Gabriele Kotsis and Ismail Khalil}
}

Contributors

The following persons have contributed to AUTO-DataGenCARS+:

• María del Carmen Rodríguez Hernández - mcrodriguez@ita.es
• Sergio Ilarri - silarri@unizar.es
• Raquel Trillo Lado - raqueltl@unizar.es
• Ramón Hermoso - rhermoso@unizar.es
• Marcos Caballero Yus - mcaballero@ita.es
• Beatriz Franco García - bfranco@ita.es

Acknowledgments

• Project PID2020-113037RB-I00 (funded by MICIU/AEI/10.13039/501100011033) — Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges (NEAT-AMBIENCE).
• Government of Aragon (COSMOS research group; last group reference: T64_23R; previous group reference: T64_20R).
• This work has also been partially funded by the Department of Big Data and Cognitive Systems at the Technological Institute of Aragon, by DGA-FSE IODIDE research group of the Government of Aragon (grant number T17_23R) and by the European Regional Development Fund (ERDF).