Computer Science > Computation and Language
arXiv:2603.09688 (cs)
[Submitted on 10 Mar 2026]
Title:Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation
Authors:Denica Kjorvezir, Danilo Najkov, Eva Valencič, Erika Jesenko, Barbara Koroišić Seljak, Tome Eftimov, Riste Stojanov
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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.
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| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09688 [cs.CL] |
| (or arXiv:2603.09688v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09688
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| Related DOI: | https://doi.org/10.1109/BigData66926.2025.11401478
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From: Denica Kjorvezir [view email][v1] Tue, 10 Mar 2026 13:56:20 UTC (1,048 KB)
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View a PDF of the paper titled Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation, by Denica Kjorvezir and 6 other authors
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