用多类别分类提升个性化菜谱推荐准确率
Enhancing Personalized Recipe Recommendation Through Multi-Class Classification
- 基于用户食材和偏好进行多类别分类推荐
- 支持菜品与食材跨类别归属，提升推荐多样性
- 适合美食推荐系统开发者与用户行为研究者
本文针对多样化饮食偏好下的个性化菜谱推荐挑战，提出融合关联分析与分类技术的解决方案。通过挖掘不同食材间的关联关系，增强用户体验；同时根据用户定义的食材与偏好对菜谱进行多类别分类。特别关注菜谱与食材可能属于多个类别的复杂情况，要求推荐系统具备更精细的分类能力，以真实反映菜谱的多重属性。研究不仅实现精准个性化推荐，还深入探讨了实现过程中的关键机制。
原文摘要 · Abstract (English)
This paper intends to address the challenge of personalized recipe recommendation in the realm of diverse culinary preferences. The problem domain involves recipe recommendations, utilizing techniques such as association analysis and classification. Association analysis explores the relationships and connections between different ingredients to enhance the user experience. Meanwhile, the classification aspect involves categorizing recipes based on user-defined ingredients and preferences. A unique aspect of the paper is the consideration of recipes and ingredients belonging to multiple classes, recognizing the complexity of culinary combinations. This necessitates a sophisticated approach to classification and recommendation, ensuring the system accommodates the nature of recipe categorization. The paper seeks not only to recommend recipes but also to explore the process involved in achieving accurate and personalized recommendations.
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