用心理人格模型+贝叶斯算法，个性化推荐香水气味组合。
Bayesian algorithmic perfumery: A Hierarchical Relevance Vector Machine for the Estimation of Personalized Fragrance Preferences based on Three Sensory Layers and Jungian Personality Archetypes
- 分三层气味+荣格人格类型构建贝叶斯预测模型。
- 通过用户交互动态更新推荐，提升个性化精度。
- 适合心理学与感官产品设计研究者参考。
本研究提出一种基于贝叶斯算法的个性化香水推荐方法，结合分层相关向量机（Hierarchical Relevance Vector Machine）与荣格人格原型（Jungian personality archetypes），如英雄、照顾者、探索者等，建立个体对前调、中调、基调气味偏好与人格特质之间的映射关系。算法利用贝叶斯更新机制，在用户与香调互动过程中持续优化预测结果，实现基于先验数据与人格评估的自适应、可解释性推荐。该方法融合心理学理论与贝叶斯机器学习，有效建模个体偏好复杂性，同时捕捉用户个体与群体层面的趋势。研究表明，分层贝叶斯框架在基于感官体验的个性化产品设计中具有潜力，推动了感官产业中机器学习应用的发展。
原文摘要 · Abstract (English)
This study explores a Bayesian algorithmic approach to personalized fragrance recommendation by integrating hierarchical Relevance Vector Machines (RVM) and Jungian personality archetypes. The paper proposes a structured model that links individual scent preferences for top, middle, and base notes to personality traits derived from Jungian archetypes, such as the Hero, Caregiver, and Explorer, among others. The algorithm utilizes Bayesian updating to dynamically refine predictions as users interact with each fragrance note. This iterative process allows for the personalization of fragrance experiences based on prior data and personality assessments, leading to adaptive and interpretable recommendations. By combining psychological theory with Bayesian machine learning, this approach addresses the complexity of modeling individual preferences while capturing user-specific and population-level trends. The study highlights the potential of hierarchical Bayesian frameworks in creating customized olfactory experiences, informed by psychological and demographic factors, contributing to advancements in personalized product design and machine learning applications in sensory-based industries.
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