Personalized Fragrance Recommendation Using a Hierarchical Bayesian Model with Relevance Vector Machine and Jungian Archetypes

Core Concepts

This paper proposes a novel approach to personalized fragrance recommendation by combining Jungian personality archetypes, sensory layers of fragrances (top, middle, and base notes), and a hierarchical Bayesian model using a Relevance Vector Machine (RVM) for dynamic preference prediction.

Abstract

• Bibliographic Information: Gonzales Martinez, R. (2024). Bayesian algorithmic perfumery: A Hierarchical Relevance Vector Machine for the Estimation of Personalized Fragrance Preferences based on Three Sensory Layers and Jungian Personality Archetypes (arXiv:2411.03965v1). arXiv. https://arxiv.org/abs/2411.03965v1

• Research Objective: This paper aims to develop a personalized fragrance recommendation system that leverages the predictive power of Bayesian machine learning and incorporates individual personality traits based on Jungian archetypes. The system dynamically refines fragrance recommendations as users interact with different scent notes (top, middle, and base) to provide a tailored olfactory experience.

• Methodology: The study proposes a hierarchical Bayesian model using a Relevance Vector Machine (RVM). This model integrates user-specific data, including personality traits derived from Jungian archetypes and preferences for different fragrance notes, with population-level trends. The RVM employs Bayesian updating to dynamically adjust and refine fragrance preference predictions as users experience each sequential note of a fragrance.

• Key Findings: The paper outlines a novel framework for personalized fragrance recommendation that combines:

  • Jungian Archetypes: Personality traits associated with different archetypes (e.g., Hero, Caregiver, Explorer) are used to inform initial fragrance preferences.
  • Sensory Layers: The model accounts for the temporal evolution of fragrance perception by incorporating preferences for top, middle, and base notes.
  • Hierarchical Bayesian RVM: This approach allows for personalized predictions by combining individual preferences with population-level trends and dynamically updating predictions based on user feedback.

• Main Conclusions: The study highlights the potential of hierarchical Bayesian frameworks, particularly using RVMs, in creating customized olfactory experiences. By integrating psychological theory with Bayesian machine learning, this approach offers a structured and adaptive method for understanding and predicting individual fragrance preferences.

• Significance: This research contributes to the advancement of personalized product design and demonstrates the application of machine learning in sensory-based industries like perfumery. The proposed model can potentially enhance customer satisfaction by providing tailored fragrance recommendations.

• Limitations and Future Research: The paper acknowledges the need for empirical validation of the proposed model with real-world user data. Future research could explore the inclusion of additional personality models, cultural influences on scent preferences, and the impact of contextual factors on fragrance choices.

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Bayesian algorithmic perfumery: A Hierarchical Relevance Vector Machine for the Estimation of Personalized Fragrance Preferences based on Three Sensory Layers and Jungian Personality Archetypes

by Rolando Gonz... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03965.pdf