KitchenScale: Learning to predict ingredient quantities from recipe contexts

Donghee Choi, Mogan Gim, Samy Badreddine, Hajung Kim, Donghyeon Park, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. We introduce KitchenScale, a fine-tuned Pre-trained Language Model (PLM) that predicts a target ingredient's quantity and measurement unit given its recipe context. To effectively train our KitchenScale model, we formulate an ingredient quantity prediction task that consists of three sub-tasks which are ingredient measurement type classification, unit classification, and quantity regression task. Furthermore, we utilized transfer learning of cooking knowledge from recipe texts to PLMs. We adopted the Discrete Latent Exponent (DExp) method to cope with high variance of numerical scales in recipe corpora. Experiments with our newly constructed dataset and recommendation examples demonstrate KitchenScale's understanding of various recipe contexts and generalizability in predicting ingredient quantities. We implemented a web application for KitchenScale to demonstrate its functionality in recommending ingredient quantities expressed in numerals (e.g., 2) with units (e.g., ounce).

Original languageEnglish
Article number120041
JournalExpert Systems With Applications
Publication statusPublished - 2023 Aug 15

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea, South Korea ( NRF-2023R1A2C3004176 , NRF-2022R1F1A1069639 ), the MSIT (Ministry of Science and ICT), Korea , under the ICT Creative Consilience program( IITP-2023-2020-0-01819 ) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and Sony AI ( ).

Publisher Copyright:
© 2023 Elsevier Ltd


  • Cooking knowledge
  • Food computing
  • Food measurement
  • Ingredient quantity prediction
  • Pre-trained language models
  • Representation learning

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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