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ARXIV:2603.09688 · FOOD AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09688FOOD AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A method for assessing recipe similarity using semantic, lexical, and domain perspectives.
Opportunity summary
Pain A method for assessing recipe similarity using semantic, lexical, and domain perspectives.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A method for assessing recipe similarity using semantic, lexical, and domain perspectives. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes.
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. A web-based interface was developed to allow domain experts to validate the combined similarity results.
Food AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A method for assessing recipe similarity using semantic, lexical, and domain perspectives.
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10.48550/arXiv.2603.09688A method for assessing recipe similarity using semantic, lexical, and domain perspectives.
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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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 3.0
PROBLEM
A method for assessing recipe similarity using semantic, lexical, and domain perspectives. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes.
METHOD
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 ingre...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. A web-based interface was developed to allow domain experts to validate the combined similarity results.
WHY NOW
Food AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A method for assessing recipe similarity using semantic, lexical, and domain perspectives. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the analysis of ingredients, preparation methods, and nutritional attributes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. A web-based interface was developed to allow domain experts to validate the combined similarity results.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Food AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A method for assessing recipe similarity using semantic, lexical, and domain perspectives.
Segment
Food AI
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
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CITED BY
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Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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