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.

翻译：本研究探索了一种结合分层相关向量机与荣格人格原型的贝叶斯算法，用于实现个性化香水推荐。论文提出了一种结构化模型，将个体对前调、中调、基调的香气偏好与源自荣格原型（如英雄、照顾者、探索者等）的人格特质相关联。该算法利用贝叶斯更新机制，在用户与各香调交互时动态优化预测。这种迭代过程能够基于先验数据与人格评估实现香水体验的个性化，从而产生自适应且可解释的推荐结果。通过将心理学理论与贝叶斯机器学习相结合，本方法在捕捉用户特异性与群体层面趋势的同时，有效解决了个体偏好建模的复杂性。研究凸显了分层贝叶斯框架在创建定制化嗅觉体验方面的潜力——该框架融合心理学与人口统计学因素，为个性化产品设计及感官产业中的机器学习应用提供了新的发展方向。