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Hauptverfasser: Gawrysiak, Zuzanna, Żywot, Agata, Ławrynowicz, Agnieszka
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2407.00107
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author Gawrysiak, Zuzanna
Żywot, Agata
Ławrynowicz, Agnieszka
author_facet Gawrysiak, Zuzanna
Żywot, Agata
Ławrynowicz, Agnieszka
contents We present WineGraph, an extended version of FlavorGraph, a heterogeneous graph incorporating wine data into its structure. This integration enables food-wine pairing based on taste and sommelier-defined rules. Leveraging a food dataset comprising 500,000 reviews and a wine reviews dataset with over 130,000 entries, we computed taste descriptors for both food and wine. This information was then utilised to pair food items with wine and augment FlavorGraph with additional data. The results demonstrate the potential of heterogeneous graphs to acquire supplementary information, proving beneficial for wine pairing.
format Preprint
id arxiv_https___arxiv_org_abs_2407_00107
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle WineGraph: A Graph Representation For Food-Wine Pairing
Gawrysiak, Zuzanna
Żywot, Agata
Ławrynowicz, Agnieszka
Machine Learning
Artificial Intelligence
We present WineGraph, an extended version of FlavorGraph, a heterogeneous graph incorporating wine data into its structure. This integration enables food-wine pairing based on taste and sommelier-defined rules. Leveraging a food dataset comprising 500,000 reviews and a wine reviews dataset with over 130,000 entries, we computed taste descriptors for both food and wine. This information was then utilised to pair food items with wine and augment FlavorGraph with additional data. The results demonstrate the potential of heterogeneous graphs to acquire supplementary information, proving beneficial for wine pairing.
title WineGraph: A Graph Representation For Food-Wine Pairing
topic Machine Learning
Artificial Intelligence
url https://arxiv.org/abs/2407.00107