KitchenScale: Learning to predict ingredient quantities from recipe contexts

Abstract

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).

arXiv commentExpert Systems with Applications 2023, Demo: http://kitchenscale.korea.ac.kr/

References in corpus (4)

• RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System
• Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers
• Standing on the Shoulders of Giant Frozen Language Models
• RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer