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Data-driven recipe completion using machine learning methods

Marlies De Clercq (UGent) , Michiel Stock (UGent) , Bernard De Baets (UGent) and Willem Waegeman (UGent)
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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:

Chicago
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.
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.
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.
MLA
De Clercq, Marlies et al. “Data-driven Recipe Completion Using Machine Learning Methods.” TRENDS IN FOOD SCIENCE & TECHNOLOGY 49 (2016): 1–13. Print.
@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},
  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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