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Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation

Denica Kjorvezir, Danilo Najkov, Eva Valencič, Erika Jesenko, Barbara Koroišić Seljak, Tome Eftimov, Riste Stojanov

TL;DR

This research explores the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes by combining different sources of information and analytical approaches.

Abstract

This research focuses on developing advanced methods for assessing similarity between recipes by combining different sources of information and analytical approaches. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes. A web-based interface was developed to allow domain experts to validate the combined similarity results. After evaluating 318 recipe pairs, experts agreed on 255 (80%). The evaluation of expert assessments enables the estimation of which similarity aspects--lexical, semantic, or nutritional--are most influential in expert decision-making. The application of these methods has broad implications in the food industry and supports the development of personalized diets, nutrition recommendations, and automated recipe generation systems.

Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation

TL;DR

This research explores the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes by combining different sources of information and analytical approaches.

Abstract

This research focuses on developing advanced methods for assessing similarity between recipes by combining different sources of information and analytical approaches. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes. A web-based interface was developed to allow domain experts to validate the combined similarity results. After evaluating 318 recipe pairs, experts agreed on 255 (80%). The evaluation of expert assessments enables the estimation of which similarity aspects--lexical, semantic, or nutritional--are most influential in expert decision-making. The application of these methods has broad implications in the food industry and supports the development of personalized diets, nutrition recommendations, and automated recipe generation systems.
Paper Structure (33 sections, 10 figures, 14 tables)

This paper contains 33 sections, 10 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Lexical Similarity
  4. Domain Similarity
  5. Semantic Similarity
  6. Data
  7. Methods
  8. Semantic Similarity through Transformer-Based Embeddings
  9. Lexical Similarity through Ingredient Similarity
  10. Domain Similarity
  11. Nutritional Value per Recipe
  12. Nutritional Value per Ingredient
  13. Fusing the Similarity Methods
  14. Analysis of Similarity Results
  15. Analysis of Specific Examples
  16. ...and 18 more sections

Figures (10)

  • Figure 1: Graphical representation of the semantic similarity algorithm
  • Figure 2: Example of a recipe from the Recipe1M dataset
  • Figure 3: Visual representation of the algorithm used for calculating lexical similarity
  • Figure 4: Graphical representation of the nutritional similarity between two recipes
  • Figure 5: Visual representation of the algorithm used for nutritional similarity between recipes based on ingredients
  • ...and 5 more figures