language:
- en
pretty_name: ScentByte Perfume SVC Dataset
tags:
- perfume
- scent
- fragrance
- SVC
- AI
- olfactory
- dataset
size_categories:
- n<1K
Dior Perfume SVC Dataset: Full Methodology and Reference
The Dior Perfume SVC Dataset provides structured scent vector codes (SVCs) for Dior perfumes. Each perfume is represented numerically, enabling quantitative fragrance analysis, AI applications in perfumery, and digital scent comparison.
This document describes not only the dataset but also the full methodology, including mathematical formulation, feature selection, and encoding procedures.
- Introduction
Perfumes are traditionally analyzed through subjective human perception, which varies across individuals and environments. Unlike colors, which can be represented in RGB or HEX codes, fragrances lack a standardized numeric representation.
The Scent Vector Code (SVC) is designed to quantify perfume scent profiles into reproducible vectors. This allows:
Objective fragrance comparison
Clustering and classification of perfumes
Integration into AI and machine learning pipelines
The Dior dataset contains 335 perfumes, each encoded with 43 fragrance attributes and their corresponding SVCs.
- Components and Necessary Inputs
To create an SVC, the following inputs and components are necessary:
Perfume Composition Data
List of ingredients and notes for each perfume (top, middle, base notes)
Concentration or relative intensity of each note (if available)
Feature Space Definition
A standardized list of 43 primary fragrance attributes
Categories include floral, woody, citrus, spicy, animalic, balsamic, etc.
Attribute Weighting Rules
Top notes: weight = 0.4
Middle/heart notes: weight = 0.35
Base notes: weight = 0.25
Normalization Constraints
Each attribute is scaled to a 0–100 range
Ensures consistency across different perfumes
- Mathematical Formulation of SVC
The SVC of a perfume can be represented as a vector:
SVC = [a1, a2, ..., a43]
Where:
n = 43 (number of fragrance attributes)
ai is the normalized weighted intensity of the i-th attribute
Step 1: Weighted Intensity Calculation
For each attribute i, the intensity is calculated as:
Ii = sum over j of (wj * cij) for j = 1 to m
Where:
m = number of notes contributing to attribute i
cij = raw concentration/intensity of note j in attribute i
wj = weight of note j (top, middle, base)
Step 2: Normalization
The raw intensity vector is normalized to a 0–100 scale:
ai = ((Ii - Imin) / (Imax - Imin)) * 100
Where Imin and Imax are the minimum and maximum intensities observed for that attribute across all perfumes.
Step 3: Vector Assembly
After normalization, the SVC is assembled as:
SVC = [a1, a2, ..., a43]
Each SVC is a 43-dimensional vector
Can be stored as a string, JSON array, or serialized object
Facilitates vector operations such as cosine similarity or Euclidean distance
- Data Extraction and Preprocessing
Source Extraction
Perfume notes collected from Fragrantica
Extracted using automated scraping pipelines or manual curation
Categorization into Attributes
Each note is mapped to one or more fragrance attributes
Example: “Bergamot” → Citrus, Fresh
Weight Assignment
Top notes: w = 0.4
Middle notes: w = 0.35
Base notes: w = 0.25
Handling Missing Data
Notes missing intensity values are imputed using the average intensity of that attribute across the dataset
SVC Generation
Weighted, normalized intensities are combined into a final 43-dimensional vector
- Computational Methodology
Vector Operations
SVCs can be compared using cosine similarity:
Similarity(SVC1, SVC2) = (SVC1 · SVC2) / (||SVC1|| * ||SVC2||)
Clustering and Analysis
Perfumes can be clustered using k-means, hierarchical clustering, or PCA
Enables visualization of fragrance families
AI Applications
Train ML models for perfume recommendation
Generate synthetic SVCs to predict new fragrance combinations
- Dataset Structure Column Description name Perfume name url Fragrantica source URL svc_code 43-dimensional SVC vector (JSON or array) fragrance_attributes Numerical intensities (0–100) of 43 attributes
- Example Usage from datasets import load_dataset from scipy.spatial.distance import cosine
Load dataset
dataset = load_dataset("Alraj/ScentByte_Perfume_SVC_Dataset") entry = dataset["train"][0]
svc_vector = entry["svc_code"] # 43-dimensional SVC attributes = entry["fragrance_attributes"]
Cosine similarity between two perfumes
similarity = 1 - cosine(svc_vector, dataset["train"][1]["svc_code"]) print("Similarity:", similarity)
- Applications of SVC
Objective Perfume Comparison – measure similarity numerically
AI Recommendations – suggest perfumes with similar SVCs
Digital Perfume Libraries – catalog scents digitally
Fragrance Research – analyze patterns and trends
- License
CC BY 4.0: Free for research, educational purposes, and derivative works with proper credit
- Conclusion
The Scent Vector Code (SVC) methodology provides a reproducible, quantitative, and computationally accessible representation of perfumes. By combining data extraction, weighted feature encoding, normalization, and vector operations, SVCs create a scalable digital language for fragrances, bridging the gap between human olfaction and computational analysis.
For more detailed analysis, methodology diagrams, and examples, please refer to scentbyte.pdf in the main directory.