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Data-driven recipe completion using machine learning methods

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COLOR, NONNEGATIVE MATRIX FACTORIZATION, TEMPERATURE, Recommender systems, ALGORITHMS, PREFERENCE, Two-step regularized least squares, Non-negative matrix factorization, Recipe completion, Ingredient combinations, FLAVOR, PERCEPTION

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Citation

Please use this url to cite or link to this publication:

MLA
De Clercq, Marlies et al. “Data-driven Recipe Completion Using Machine Learning Methods.” TRENDS IN FOOD SCIENCE & TECHNOLOGY 49 (2016): 1–13. Print.
APA
De Clercq, Marlies, Stock, M., De Baets, B., & Waegeman, W. (2016). Data-driven recipe completion using machine learning methods. TRENDS IN FOOD SCIENCE & TECHNOLOGY, 49, 1–13.
Chicago author-date
De Clercq, Marlies, Michiel Stock, Bernard De Baets, and Willem Waegeman. 2016. “Data-driven Recipe Completion Using Machine Learning Methods.” Trends in Food Science & Technology 49: 1–13.
Chicago author-date (all authors)
De Clercq, Marlies, Michiel Stock, Bernard De Baets, and Willem Waegeman. 2016. “Data-driven Recipe Completion Using Machine Learning Methods.” Trends in Food Science & Technology 49: 1–13.
Vancouver
1.
De Clercq M, Stock M, De Baets B, Waegeman W. Data-driven recipe completion using machine learning methods. TRENDS IN FOOD SCIENCE & TECHNOLOGY. 2016;49:1–13.
IEEE
[1]
M. De Clercq, M. Stock, B. De Baets, and W. Waegeman, “Data-driven recipe completion using machine learning methods,” TRENDS IN FOOD SCIENCE & TECHNOLOGY, vol. 49, pp. 1–13, 2016.
@article{7167140,
  author       = {{De Clercq, Marlies and Stock, Michiel and De Baets, Bernard and Waegeman, Willem}},
  issn         = {{0924-2244}},
  journal      = {{TRENDS IN FOOD SCIENCE & TECHNOLOGY}},
  keywords     = {{COLOR,NONNEGATIVE MATRIX FACTORIZATION,TEMPERATURE,Recommender systems,ALGORITHMS,PREFERENCE,Two-step regularized least squares,Non-negative matrix factorization,Recipe completion,Ingredient combinations,FLAVOR,PERCEPTION}},
  language     = {{eng}},
  pages        = {{1--13}},
  title        = {{Data-driven recipe completion using machine learning methods}},
  url          = {{http://dx.doi.org/10.1016/j.tifs.2015.11.010}},
  volume       = {{49}},
  year         = {{2016}},
}

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