Zhang Qing, Elsweiler David, Trattner Christoph
School of Information Management, Sun Yat-sen University, Guangzhou 510006, China.
Institute for Language, Literature and Culture, University of Regensburg, 93053 Regensburg, Germany.
Foods. 2025 Apr 18;14(8):1411. doi: 10.3390/foods14081411.
Navigating cross-cultural food choices is complex, influenced by cultural nuances and various factors, with flavor playing a crucial role. Understanding cultural flavor preferences helps individuals make informed food choices in cross-cultural contexts. We examined flavor differences across China, the US, and Germany, as well as consistent flavor preference patterns using online recipes from prominent recipe portals. Distinct from applying traditional food pairing theory, we directly mapped ingredients to their individual flavor compounds using an authorized database. This allowed us to analyze cultural flavor preferences at the molecular level and conduct machine learning experiments on 25,000 recipes from each culture to reveal flavor-based distinctions. The classifier, trained on these flavor compounds, achieved 77% accuracy in discriminating recipes by country in a three-class classification task, where random choice would yield 33.3% accuracy. Additionally, using user interaction data on appreciation metrics from each recipe portal (e.g., recipe ratings), we selected the top 10% and bottom 10% of recipes as proxies for appreciated and less appreciated recipes, respectively. Models trained within each portal discriminated between the two groups, reaching a maximum accuracy of 66%, while random selection would result in a baseline accuracy of 50%. We also explored cross-cultural preferences by applying classifiers trained on one culture to recipes from other cultures. While the cross-cultural performance was modest (specifically, a max accuracy of 54% was obtained when predicting food preferences ofthe USusers with models trained on the Chinesedata), the results indicate potential shared flavor patterns, especially between Chinese and US recipes, which show similarities, while German preferences differ. Exploratory analyses further validated these findings: we constructed ingredient networks based on co-occurrence relationships to label recipes as savory or sweet, and clustered the flavor profiles of compounds as sweet or non-sweet. These analyses showed opposing trends in sweet vs. non-sweet/savory appreciation between US and German users, supporting the machine learning results. Although our findings are likely to be influenced by biases in online data sources and the limitations of data-driven methods, they may still highlight meaningful cultural differences and shared flavor preferences. These insights offer potential for developing food recommender systems that cater to cross-cultural contexts.
在跨文化背景下做出食物选择是复杂的，受到文化细微差别和各种因素的影响，其中风味起着至关重要的作用。了解文化风味偏好有助于个人在跨文化背景下做出明智的食物选择。我们通过来自知名食谱平台的在线食谱，研究了中国、美国和德国之间的风味差异以及一致的风味偏好模式。与应用传统食物搭配理论不同，我们使用授权数据库将食材直接映射到其各自的风味化合物。这使我们能够在分子水平上分析文化风味偏好，并对来自每种文化的25000份食谱进行机器学习实验，以揭示基于风味的差异。在这些风味化合物上训练的分类器，在一个三类分类任务中，按国家区分食谱的准确率达到了77%，而随机选择的准确率为33.3%。此外，利用每个食谱平台上关于评价指标的用户交互数据（如食谱评分），我们分别选择了10%最受欢迎和10%最不受欢迎的食谱作为受欢迎和不受欢迎食谱的代表。在每个平台内训练的模型区分了这两组食谱，最高准确率达到66%，而随机选择的基线准确率为50%。我们还通过将在一种文化上训练的分类器应用于其他文化的食谱来探索跨文化偏好。虽然跨文化表现一般（具体而言，当用在中国数据上训练的模型预测美国用户的食物偏好时，最高准确率为54%），但结果表明存在潜在的共同风味模式，特别是在中国和美国的食谱之间，它们表现出相似性，而德国的偏好则不同。探索性分析进一步验证了这些发现：我们基于共现关系构建食材网络，将食谱标记为咸味或甜味，并将化合物的风味特征聚类为甜味或非甜味。这些分析显示了美国和德国用户在甜味与非甜味/咸味偏好上的相反趋势，支持了机器学习结果。尽管我们的发现可能受到在线数据源偏差和数据驱动方法局限性的影响，但它们仍可能突出有意义的文化差异和共同的风味偏好。这些见解为开发适应跨文化背景的食物推荐系统提供了潜力。
