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

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

doi:10.48550/arxiv.2603.09688

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

Date Issued

2026

Author(s)

Kjorvezir, Denica

Najkov, Danilo

Valencič, Eva

Jesenko, Erika

Seljak, Barbara Koroišić

Eftimov, Tome

DOI

10.48550/arxiv.2603.09688

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.