X-Wines Dataset

A world wine dataset with 5-stars user ratings and a web collaborative platform for wider free use.

It offers a preprocessed, consistent, and open data alternative for general use by softwares, especially in educational processes and research, through scientific experimentation about recommender systems and machine learning using neural networks.

Contents:

• 100,646 (100K) wine instances containing 17 selected attributes.
• 21,013,536 (21M) ratings 5-stars instances, containing date and value a 1–5 scale ⊂ {1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5}
• 1,056,079 (1M) anonymized users.

Versions:

There are 3 versions: Full ⊂ Slim ⊂ Test.

Version     # Wines   # Wine Types    # Wine Countries   # Ratings      # Users   Multiple user-wine rating
Test           100          6               17              1,000          636             No
Slim         1,007          6               31            150,000       10,561             No
Full       100,646          6               62         21,013,536    1,056,079             Yes

It is published for download in the Dataset folder.

Citation Policy:

If you publish material based on X-Wines dataset obtained from this or another repository, then, in your references the paper below must be mandatory cited. Additionally, if you wish to include in your acknowledgments, please note the support you received by using this dataset.

MDPI and ACS Style:

de Azambuja, R.X.; Morais, A.J.; Filipe, V. X-Wines: A Wine Dataset for Recommender Systems and Machine Learning. Big Data Cogn. Comput. 2023, 7, 20. https://doi.org/10.3390/bdcc7010020.

Chicago/Turabian Style:

Azambuja, Rogério Xavier de, A. Jorge Morais, and Vítor Filipe. 2023. “X-Wines: A Wine Dataset for Recommender Systems and Machine Learning.” Big Data and Cognitive Computing 7, no. 1: 20. https://doi.org/10.3390/bdcc7010020.

APA 7th Edition Style:

de Azambuja, R. X., Morais, A. J., & Filipe, V. (2023). X-Wines: A Wine Dataset for Recommender Systems and Machine Learning. Big Data and Cognitive Computing, 7(1), Article 20. https://doi.org/10.3390/bdcc7010020.

Here is a BiBTeX citation as well:

@Article{bdcc7010020,
AUTHOR = {de Azambuja, Rogério Xavier and Morais, A. Jorge and Filipe, Vítor},
TITLE = {X-Wines: A Wine Dataset for Recommender Systems and Machine Learning},
JOURNAL = {Big Data and Cognitive Computing},
VOLUME = {7},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {20},
URL = {https://www.mdpi.com/2504-2289/7/1/20},
ISSN = {2504-2289},
ABSTRACT = {In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.},
DOI = {10.3390/bdcc7010020}
}

This will help others to obtain the X-Wines dataset for wider free use and replicate your experiments.

Collaborative Platform (go now):

X-Wines is an academic work with scientific relevance in the computing area that is interconnected with other areas of knowledge. Please, access the collaborative platform and have fun among the wines experience: https://sites.google.com/farroupilha.ifrs.edu.br/xwines.

• Navigate a free web platform without advertisements or any product sales. Know more about wines, their characteristics, elaboration, grape varieties, food pairings, wineries, producing regions, and more.
• Find your favorite or innovative wines! There are 100,646 wine labels, 21 million ratings, 30,510 wineries, 2,160 producing regions in 62 different countries around the world.

Contact:
Prof. Rogério Xavier de Azambuja
rogerio.xavier[at]farroupilha.ifrs.edu.br

IFRS-Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul (www.ifrs.edu.br)
UTAD-Universidade de Trás-os-Montes e Alto Douro/ECT-Escola de Ciências e Tecnologia (www.utad.pt)
UAb-Universidade Aberta/DCeT-Departamento de Ciências e Tecnologias (www.uab.pt)