{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:21:29Z","timestamp":1772252489127,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T00:00:00Z","timestamp":1674345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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\u20135 ratings carried out over a period of 10 years (2012\u20132021) 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.<\/jats:p>","DOI":"10.3390\/bdcc7010020","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T02:01:02Z","timestamp":1674439262000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["X-Wines: A Wine Dataset for Recommender Systems and Machine Learning"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1746-2039","authenticated-orcid":false,"given":"Rog\u00e9rio Xavier","family":"de Azambuja","sequence":"first","affiliation":[{"name":"Instituto Federal do Rio Grande do Sul (IFRS), Farroupilha 95174-274, RS, Brazil"},{"name":"Department of Science and Technology, Universidade Aberta (UAb), 1269-001 Lisbon, Portugal"},{"name":"School of Science and Technology, Universidade de Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2224-1609","authenticated-orcid":false,"given":"A. Jorge","family":"Morais","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, Universidade Aberta (UAb), 1269-001 Lisbon, Portugal"},{"name":"LIAAD\u2014INESC TEC, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-6577","authenticated-orcid":false,"given":"V\u00edtor","family":"Filipe","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Universidade de Tr\u00e1s-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal"},{"name":"INESC TEC\u2014INESC Tecnologia e Ci\u00eancia, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,22]]},"reference":[{"key":"ref_1","unstructured":"Juban, Y. (2022). International Standard for the Labelling of Wines, OIV-International Organization of Vine and Wine. Available online: https:\/\/www.oiv.int\/what-we-do\/standards."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2827872","article-title":"The MovieLens Datasets: History and Context","volume":"5","author":"Harper","year":"2016","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_3","unstructured":"(2022, October 09). Tianchi: Taobao Dataset, Available online: https:\/\/tianchi.aliyun.com\/datalab\/dataSet.html?dataId=649."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, R., and McAuley, J. (2016, January 11). Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. Proceedings of the 25th International Conference on World Wide Web, Montr\u00e9al, QC, Canada.","DOI":"10.1145\/2872427.2883037"},{"key":"ref_5","unstructured":"Dua, D., and Graff, C. (2022, October 09). UCI Machine Learning Repository, Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ziegler, C.-N., McNee, S.M., Konstan, J.A., and Lausen, G. (2005, January 10\u201314). Improving Recommendation Lists through Topic Diversification. Proceedings of the 14th International Conference on World Wide Web\u2014WWW \u201905, Chiba, Japan.","DOI":"10.1145\/1060745.1060754"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1023\/A:1011419012209","article-title":"Eigentaste: A Constant Time Collaborative Filtering Algorithm","volume":"4","author":"Goldberg","year":"2001","journal-title":"Inf. Retr."},{"key":"ref_8","unstructured":"(2022, December 09). Kaggle Open Datasets and Machine Learning Projects, Available online: https:\/\/www.kaggle.com\/datasets."},{"key":"ref_9","unstructured":"(2022, December 09). GitHub Data Packaged Core Datasets, Available online: https:\/\/github.com\/datasets."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s40747-020-00212-w","article-title":"Artificial Intelligence in Recommender Systems","volume":"7","author":"Zhang","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neucom.2021.11.041","article-title":"(Xuejun) A Survey of Recommender Systems with Multi-Objective Optimization","volume":"474","author":"Zheng","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/963770.963772","article-title":"Evaluating Collaborative Filtering Recommender Systems","volume":"22","author":"Herlocker","year":"2004","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","article-title":"Amazon.Com Recommendations: Item-to-Item Collaborative Filtering","volume":"7","author":"Linden","year":"2003","journal-title":"IEEE Internet Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix Factorization Techniques for Recommender Systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MIC.2017.72","article-title":"Two Decades of Recommender Systems at Amazon.Com","volume":"21","author":"Smith","year":"2017","journal-title":"IEEE Internet Comput."},{"key":"ref_16","unstructured":"Hardesty, L. (2022, October 02). The History of Amazon\u2019s Recommendation Algorithm, Available online: https:\/\/www.amazon.science\/the-history-of-amazons-recommendation-algorithm."},{"key":"ref_17","unstructured":"Zhao, T. (2022, October 02). Improving Complementary-Product Recommendations, Available online: https:\/\/www.amazon.science\/blog\/improving-complementary-product-recommendations."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ejor.2017.07.005","article-title":"A Framework for Configuring Collaborative Filtering-Based Recommendations Derived from Purchase Data","volume":"265","author":"Geuens","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1023\/A:1009804230409","article-title":"E-Commerce Recommendation Applications","volume":"5","author":"Schafer","year":"2001","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/TCSS.2019.2950139","article-title":"Recommender System-Based Diffusion Inferring for Open Social Networks","volume":"7","author":"Yang","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_21","first-page":"36","article-title":"A Social Media Recommender System","volume":"9","author":"Amato","year":"2018","journal-title":"Int. J. Multimed. Data Eng. Manag. IJMDEM"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"34499","DOI":"10.1007\/s11042-019-08607-9","article-title":"Multimedia Recommendation Using Word2Vec-Based Social Relationship Mining","volume":"80","author":"Baek","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.future.2016.06.015","article-title":"Multimedia Recommendation and Transmission System Based on Cloud Platform","volume":"70","author":"Yang","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sahoo, A.K., Pradhan, C., Barik, R.K., and Dubey, H. (2019). DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering. Computation, 7.","DOI":"10.3390\/computation7020025"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"28462","DOI":"10.1109\/ACCESS.2020.2968537","article-title":"Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model","volume":"8","author":"Iwendi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"91","article-title":"E-Tourism Recommender Systems: A Survey and Development Perspectives","volume":"6","author":"Artemenko","year":"2017","journal-title":"ECONTECHMOD Int. Q. J. Econ. Technol. Model. Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"47","DOI":"10.26599\/BDMA.2020.9020015","article-title":"Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework","volume":"4","author":"Fararni","year":"2021","journal-title":"Big Data Min. Anal."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kulkarni, N.H., Srinivasan, G.N., Sagar, B.M., and Cauvery, N.K. (2018, January 20\u201322). Improving Crop Productivity Through a Crop Recommendation System Using Ensembling Technique. Proceedings of the 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India.","DOI":"10.1109\/CSITSS.2018.8768790"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"03034","DOI":"10.1051\/itmconf\/20203203034","article-title":"Collaborative Recommendation System for Agriculture Sector","volume":"32","author":"Jaiswal","year":"2020","journal-title":"ITM Web Conf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14445\/23488387\/IJCSE-V7I5P101","article-title":"Crop Yield Prediction, Forecasting and Fertilizer Recommendation Using Voting Based Ensemble Classifier","volume":"7","author":"Archana","year":"2020","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.compag.2018.06.049","article-title":"Agricultural Recommendation System for Crop Protection","volume":"152","author":"Lacasta","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","unstructured":"(2022, October 20). Pytesseract: A Python wrapper for Google\u2019s Tesseract-OCR, Available online: https:\/\/pypi.org\/project\/pytesseract."},{"key":"ref_33","unstructured":"(2022, October 20). OpenCV: Open source library, Available online: https:\/\/opencv.org."},{"key":"ref_34","unstructured":"(2022, October 20). Googletrans: Free Google Translate API for Python, Available online: https:\/\/pypi.org\/project\/googletrans."},{"key":"ref_35","unstructured":"(2022, October 20). Spacy-langdetect: Fully Customizable Language Detection Pipeline for spaCy, Available online: https:\/\/pypi.org\/project\/spacy-langdetect."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"160018","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for Scientific Data Management and Stewardship","volume":"3","author":"Dumontier","year":"2016","journal-title":"Sci. Data"},{"key":"ref_37","unstructured":"Puckette, M., and Hammack, J. (2015). Wine Folly: The Essential Guide to Wine, Wine Folly LLC."},{"key":"ref_38","unstructured":"Macneil, K. (2015). The Wine Bible, Workman Publishing. [2nd ed.]."},{"key":"ref_39","unstructured":"(2022, September 15). Wine Encyclopedia Lexicon in the World, Available online: https:\/\/glossary.wein.plus."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.inffus.2020.10.014","article-title":"Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges","volume":"67","author":"Martinez","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"12325","DOI":"10.1007\/s10586-017-1616-7","article-title":"Adaptable and Proficient Hellinger Coefficient Based Collaborative Filtering for Recommendation System","volume":"22","author":"Maheswari","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994, January 22\u201326). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, USA.","DOI":"10.1145\/192844.192905"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"113764","DOI":"10.1016\/j.eswa.2020.113764","article-title":"A Survey of Research Hotspots and Frontier Trends of Recommendation Systems from the Perspective of Knowledge Graph","volume":"165","author":"Shao","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3158369","article-title":"Deep Learning Based Recommender System: A Survey and New Perspectives","volume":"52","author":"Zhang","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TPAMI.2020.3020738","article-title":"Building and Interpreting Deep Similarity Models","volume":"44","author":"Eberle","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","unstructured":"Hiriyannaiah, S., Siddesh, G.M., and Srinivasa, K.G. (2020). Deep Visual Ensemble Similarity (DVESM) Approach for Visually Aware Recommendation and Search in Smart Community. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s42979-020-00399-2","article-title":"Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems","volume":"2","author":"Gharahighehi","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P. (2019, January 3\u20137). BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3357895"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s11257-018-9209-6","article-title":"Evaluation of Session-Based Recommendation Algorithms","volume":"28","author":"Ludewig","year":"2018","journal-title":"User Model. User-Adapt. Interact."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_52","unstructured":"(2022, October 15). TensorFlow Recommenders, Available online: https:\/\/www.tensorflow.org\/recommenders."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B., and Kantor, P.B. (2010). Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3"},{"key":"ref_54","first-page":"1","article-title":"Cornac: A Comparative Framework for Multimodal Recommender Systems","volume":"21","author":"Salah","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., and Volinsky, C. (2008, January 15\u201319). Collaborative Filtering for Implicit Feedback Datasets. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.22"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Koren, Y. (2008, January 24\u201327). Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401944"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lara-Cabrera, R., Gonz\u00e1lez-Prieto, \u00c1., and Ortega, F. (2020). Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems. Appl. Sci., 10.","DOI":"10.3390\/app10144926"},{"key":"ref_58","unstructured":"(2022, December 15). Cornac. A Comparative Framework for Multimodal Recommender Systems, Available online: https:\/\/github.com\/PreferredAI\/cornac."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liang, D., Krishnan, R.G., Hoffman, M.D., and Jebara, T. (2018, January 23\u201327). Variational Autoencoders for Collaborative Filtering. Proceedings of the 2018 World Wide Web Conference, Lyon, France.","DOI":"10.1145\/3178876.3186150"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:13:45Z","timestamp":1760120025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,22]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["bdcc7010020"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7010020","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1007\/s43995-025-00219-9","asserted-by":"object"}]},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,22]]}}}