融合语义、词汇与领域信息，提升菜谱相似度评估准确率
Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation
- 结合食材、做法和营养属性，从三方面分析菜谱相似性
- 专家验证318对菜谱，一致率达80%，确认语义与营养影响更大
- 适合个性化饮食推荐与智能菜谱生成系统研发者参考
本研究致力于通过融合不同信息源与分析方法，提升菜谱相似度评估能力。重点考察菜谱的语义、词汇及领域相似性，基于食材、制作方法与营养特征进行分析。开发了基于网页的界面，供领域专家验证综合相似度结果。对318对菜谱进行评估后，专家达成一致意见255对（80%）。通过对专家判断的分析，可估算出在专家决策中，词汇、语义或营养等相似性维度的相对影响力。该方法在食品行业有广泛应用前景，支持个性化饮食制定、营养建议及自动化菜谱生成系统的发展。
原文摘要 · Abstract (English)
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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