X-Wines: Wine Data for Wide Usage
Abstract
In the current scenario of technological progress, similar to most agricultural products, wine has a very small volume of data available or only a few attributes, which restricts scientific exploration. This also applies to recommender systems. This paper presents and evaluates a new database called X-Wines in its first year of publication. It consists of 100,646 wine labels produced in 62 countries, with 21 million real consumer ratings sourced from the open Web in the year 2022. X-Wines is available for wider free use in recommender systems, machine learning, and general purposes, contributing to data science.
References
Archana, K. and Saranya, K.G. (2020). “Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier”, In: International Journal of Computer Science and Engineering 7, 5, p. 1–4.
Artemenko, O., Kunanets, O., and Pasichnyk, V. (2017). “E-tourism recommender systems: a survey and development perspectives”, In:ECONTECHMOD International Journal on Economics of Technology and Modelling Processes 6, 2, p. 91–95.
de Azambuja, R.X., Morais, A.J., and Filipe, V. (2022). “Adaptive Recommendation in Online Environments”, In: Distributed Computing and Artificial Intelligence, Vol. 2: 18th International Conference, Springer International Publishing, p. 185–189.
de Azambuja, R.X., Morais, A.J., and Filipe, V. (2023). “X-Wines: A Wine Dataset for Recommender Systems and Machine Learning”, In: Big Data and Cognitive Computing 7, 1.
Baek, J.-W. and Chung, K.-Y. (2021). “Multimedia recommendation using Word2Vec-based social relationship mining”, In: Multimedia Tools and Applications 80, 26, p. 34499–34515.
Dumontier, M., Aalbersberg, Ij.J., Appleton, G., et al. (2016). “The FAIR Guiding Principles for scientific data management and stewardship”, In: Scientific Data 3, 1, 160018.
Fararni, K.A., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., and Sabri, A. (2021). “Hybrid recommender system for tourism based on big data and AI: A conceptual framework”, In: Big Data Mining and Analytics 4, 1, p. 47–55.
Geuens, S., Coussement, K., and De Bock, K.W. (2018). “A framework for configuring collaborative filtering-based recommendations derived from purchase data”, In: European Journal of Operational Research 265, 1, p. 208–218.
Gkikas, D.C., Theodoridis, P.K., Theodoridis, T., and Gkikas, M.C. (2023). “Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification”, In: Informatics 10, 3.
He, M., Jiang, W., and Gu, W. (2023). “Making Wines Smarter: Evidence from an Interpretable Learning Paradigm”, In: 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), p. 60–65.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., and Riedl, J.T. (2004). “Evaluating Collaborative Filtering Recommender Systems”, In: ACM Transactions on Information Systems 22, 1, p. 5–53.
Iwendi, C., Khan, S., Anajemba, J.H., Bashir, A.K., and Noor, F. (2020). “Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model”, In: IEEE Access 8, p. 28462–28474.
Jaiswal, S., Kharade, T., Kotambe, N., and Shinde, S. (2020). “Collaborative Recommendation System For Agriculture Sector”, ITM Web Conf. 32, 03034.
Juban, Y. (2022). “International Standard for the Labelling of Wines”, OIV-International Organization of Vine and Wine, Paris.
Koren, Y., Bell, R., and Volinsky, C. (2009). “Matrix Factorization Techniques for Recommender Systems”, In: Computer 42, 8, p. 30–37.
Kulkarni, N.H., Srinivasan, G.N., Sagar, B.M., and Cauvery, N.K. (2018). “Improving Crop Productivity Through A Crop Recommendation System Using Ensembling Technique”, In: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), p. 114–119. DOI
Lacasta, J., Lopez-Pellicer, F.J., Espejo-García, B., Nogueras-Iso, J., and Zarazaga-Soria, F.J. (2018). “Agricultural recommendation system for crop protection”, In: Computers and Electronics in Agriculture 152, p. 82–89.
Macneil, K. (2015). “The Wine Bible”, Workman Publishing, New York.
Papadakos, P., Chrysakis, I., Patkos, T., et al. (2023). “Message-in-a-bottle: engaging stories around sustainable and safe wine products”, In: Discover Sustainability 4, 1, 43.
Puckette, M. and Hammack, J. (2015). “Wine Folly: The Essential Guide to Wine”, Wine Folly LLC, New York.
Sahoo, A.K., Pradhan, C., Barik, R.K., and Dubey, H. (2019). “DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering”, In: Computation 7, 2, 25.
Schafer, J.B., Konstan, J.A., and Riedl, J. (2001). “E-Commerce Recommendation Applications”, In Data Mining and Knowledge Discovery 5, 1, p. 115–153.
Shao, B., Li, X., and Bian, G. (2021). “A Survey of Research Hotspots and Frontier Trends of Recommendation Systems from the Perspective of Knowledge Graph”, In: Expert Systems with Applications 165, 113764.
Smith, B. and Linden, G. (2017). “Two Decades of Recommender Systems at Amazon.com”, In: IEEE Internet Computing 21, 3, p. 12–18.
Sperlì, G., Amato, F., Mercorio, F., Mezzanzanica, M., Moscato, V., and Picariello, A. (2018). “A Social Media Recommender System”, In: International Journal of Multimedia Data Engineering and Management (IJMDEM) 9, 1, 36–50.
Wine Encyclopedia Lexicon in the World. (2022). Disponível em: [link]. Acesso em 26/01/2024.
Yang, J., Wang, H., Lv, Z., et al. (2017). “Multimedia recommendation and transmission system based on cloud platform”, In: Future Generation Computer Systems 70, p. 94–103.
Yang, X., Dong, M., Chen, X., and Ota, K. (2020). “Recommender System-Based Diffusion Inferring for Open Social Networks”, In: IEEE Transactions on Computational Social Systems 7, 1, p. 24–34.
Zhang, Q., Lu, J., and Jin, Y. (2021). “Artificial Intelligence in Recommender Systems”, In: Complex & Intelligent Systems 7, 1, p. 439–457.
Artemenko, O., Kunanets, O., and Pasichnyk, V. (2017). “E-tourism recommender systems: a survey and development perspectives”, In:ECONTECHMOD International Journal on Economics of Technology and Modelling Processes 6, 2, p. 91–95.
de Azambuja, R.X., Morais, A.J., and Filipe, V. (2022). “Adaptive Recommendation in Online Environments”, In: Distributed Computing and Artificial Intelligence, Vol. 2: 18th International Conference, Springer International Publishing, p. 185–189.
de Azambuja, R.X., Morais, A.J., and Filipe, V. (2023). “X-Wines: A Wine Dataset for Recommender Systems and Machine Learning”, In: Big Data and Cognitive Computing 7, 1.
Baek, J.-W. and Chung, K.-Y. (2021). “Multimedia recommendation using Word2Vec-based social relationship mining”, In: Multimedia Tools and Applications 80, 26, p. 34499–34515.
Dumontier, M., Aalbersberg, Ij.J., Appleton, G., et al. (2016). “The FAIR Guiding Principles for scientific data management and stewardship”, In: Scientific Data 3, 1, 160018.
Fararni, K.A., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., and Sabri, A. (2021). “Hybrid recommender system for tourism based on big data and AI: A conceptual framework”, In: Big Data Mining and Analytics 4, 1, p. 47–55.
Geuens, S., Coussement, K., and De Bock, K.W. (2018). “A framework for configuring collaborative filtering-based recommendations derived from purchase data”, In: European Journal of Operational Research 265, 1, p. 208–218.
Gkikas, D.C., Theodoridis, P.K., Theodoridis, T., and Gkikas, M.C. (2023). “Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification”, In: Informatics 10, 3.
He, M., Jiang, W., and Gu, W. (2023). “Making Wines Smarter: Evidence from an Interpretable Learning Paradigm”, In: 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), p. 60–65.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., and Riedl, J.T. (2004). “Evaluating Collaborative Filtering Recommender Systems”, In: ACM Transactions on Information Systems 22, 1, p. 5–53.
Iwendi, C., Khan, S., Anajemba, J.H., Bashir, A.K., and Noor, F. (2020). “Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model”, In: IEEE Access 8, p. 28462–28474.
Jaiswal, S., Kharade, T., Kotambe, N., and Shinde, S. (2020). “Collaborative Recommendation System For Agriculture Sector”, ITM Web Conf. 32, 03034.
Juban, Y. (2022). “International Standard for the Labelling of Wines”, OIV-International Organization of Vine and Wine, Paris.
Koren, Y., Bell, R., and Volinsky, C. (2009). “Matrix Factorization Techniques for Recommender Systems”, In: Computer 42, 8, p. 30–37.
Kulkarni, N.H., Srinivasan, G.N., Sagar, B.M., and Cauvery, N.K. (2018). “Improving Crop Productivity Through A Crop Recommendation System Using Ensembling Technique”, In: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), p. 114–119. DOI
Lacasta, J., Lopez-Pellicer, F.J., Espejo-García, B., Nogueras-Iso, J., and Zarazaga-Soria, F.J. (2018). “Agricultural recommendation system for crop protection”, In: Computers and Electronics in Agriculture 152, p. 82–89.
Macneil, K. (2015). “The Wine Bible”, Workman Publishing, New York.
Papadakos, P., Chrysakis, I., Patkos, T., et al. (2023). “Message-in-a-bottle: engaging stories around sustainable and safe wine products”, In: Discover Sustainability 4, 1, 43.
Puckette, M. and Hammack, J. (2015). “Wine Folly: The Essential Guide to Wine”, Wine Folly LLC, New York.
Sahoo, A.K., Pradhan, C., Barik, R.K., and Dubey, H. (2019). “DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering”, In: Computation 7, 2, 25.
Schafer, J.B., Konstan, J.A., and Riedl, J. (2001). “E-Commerce Recommendation Applications”, In Data Mining and Knowledge Discovery 5, 1, p. 115–153.
Shao, B., Li, X., and Bian, G. (2021). “A Survey of Research Hotspots and Frontier Trends of Recommendation Systems from the Perspective of Knowledge Graph”, In: Expert Systems with Applications 165, 113764.
Smith, B. and Linden, G. (2017). “Two Decades of Recommender Systems at Amazon.com”, In: IEEE Internet Computing 21, 3, p. 12–18.
Sperlì, G., Amato, F., Mercorio, F., Mezzanzanica, M., Moscato, V., and Picariello, A. (2018). “A Social Media Recommender System”, In: International Journal of Multimedia Data Engineering and Management (IJMDEM) 9, 1, 36–50.
Wine Encyclopedia Lexicon in the World. (2022). Disponível em: [link]. Acesso em 26/01/2024.
Yang, J., Wang, H., Lv, Z., et al. (2017). “Multimedia recommendation and transmission system based on cloud platform”, In: Future Generation Computer Systems 70, p. 94–103.
Yang, X., Dong, M., Chen, X., and Ota, K. (2020). “Recommender System-Based Diffusion Inferring for Open Social Networks”, In: IEEE Transactions on Computational Social Systems 7, 1, p. 24–34.
Zhang, Q., Lu, J., and Jin, Y. (2021). “Artificial Intelligence in Recommender Systems”, In: Complex & Intelligent Systems 7, 1, p. 439–457.
Published
2024-04-10
How to Cite
AZAMBUJA, Rogério Xavier de; MORAIS, A. Jorge; FILIPE, Vítor.
X-Wines: Wine Data for Wide Usage. In: REGIONAL DATABASE SCHOOL (ERBD), 19. , 2024, Farroupilha/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 131-140.
ISSN 2595-413X.
DOI: https://doi.org/10.5753/erbd.2024.238852.
