AI for Fragrance Design

Richard Goodwin
Richard Segal

Joana Maria

Payel Das

Jing Fu

Raya Horesh

Christian Harris

IBM Research
Yorktown Heights, NY 10598
rgoodwin@us.ibm.com

Abstract
AI for fragrance design builds on IBM’s work on Chef Watson to create a system fordesigning new fragrances. Our system generates alternative fragrance formulations
and selects the formulation that best optimizes design objectives. We use machinelearning to predict both the technical performance of candidate fragrances (shelfstability and skin irritation) as well as the human response (pleasantness and genderappropriateness). We identify if a fragrance is novel by comparing its notes to alarge set of commercially available fragrances. The end goal is to use artificial
intelligence to design award winning new fragrances.

1Introduction

Fragrances are designed by Master Perfumers who typically train for 10 years before they become
proficient at their craft [1]. There are many technical aspects that must be mastered, such as how toformulate fragrances that won’t irritate the skin and that won’t turn cloudy or turn color when left on
the shelf for months. Beyond the technical requirements, a perfumer must create a fragrance thatsmells good, triggers a desired emotional response and is unique.
The goal of our work is to help create new and novel fragrances that perform well technically, but more
importantly, smell good and are unique. We are building on earlier work on computation creativity
for food that used models of flavor pairing and psychological modes of olfactory pleasantness toidentify recipes of dishes that would taste and smell good [2]. In the fragrance space, we don’t havethe equivalent of the flavor pairing hypothesis, we can’t use a simple model of olfactory pleasantness,and surprise comes not in the combination of ingredients, but from the combination of fragrance
notes for a perfume [3].

2

Fragrance Creation

Fragrance creation begins when a perfumer specifies a set of target notes, the technical requirements
for the new fragrance, and the target market. We use a generate and evaluate architecture to create
new fragrances. Each fragrance is evaluated and scored using a multi-attribute evaluation functionthat considers how well it hits the target notes, its overall pleasantness, its technical performance, andits uniqueness in the market.
To know whether a fragrance is unique, you need to know the set of existing fragrances. Fragrances
are typically characterized by the set of fragrance notes exhibited in the top, middle and base of thefragrance. A unique combination of fragrance notes indicates a unique fragrance. Figure 1 shows
a plot of 3319 commercially available perfumes we have collected from a publicly available web
31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

Figure 1: Commercially available perfumes,projected by fragrance categories and coloredby success.

Figure 2: Commercially available perfumes,projected by notes and colored by target gender.
Table 2: Rating Class Confusion Matrix.

Table 1: Gender Confusion Matrix
GenderPredicted
FemalePredicted
Male

Rating

Predicted
High

Predicted
Low

FemaleMale

1894342202

High
Low

1021
547

716
1019

site [4]. The plot is a t-SNE projection of the dominant odor characteristics of each perfume, coloredby their average rating [5]. This non-linear dimensionality reduction method allows us to uncovercluster relationships in the high-dimensional data set and to project them in the 2D dimensional
space with the intrinsic structure preserved. You can see that the perfumes tend to form clusters
around some of the more highly rated ones, representing a “copy cat” effect. The white spaces in the
diagram indicate combinations of fragrance notes that have not been explored commercially. Creating
fragrances in these white spaces will create new and novel combinations of fragrance notes.
Just producing a unique fragrance is not enough. The fragrance needs to be appropriate for theintended consumer and highly rated. Figure 2 shows a t-SNE projection of the sub notes of theperfumes colored by intended gender. A dominant note might be citrus, while a sub-note might bemandarin orange or lemon zest, both types of citrus. Using the collected data, we train classifiers
to predict target gender and average rating for unseen fragrances, characterized by their notes. The
performance of the classifiers is shown in tables 1 and 2. We can see that gender prediction isrelatively good at over 80%, while ratings prediction is only about 62%. From the diagrams, you cansee that gender is fairly well separated. While for ratings, the clusters of copy cat formulas makes
distinguishing highly rated formulas more difficult.
Any perfume that is not shelf stable, is flammable, or irritates the skin will be rejected both by
regulators and the market. We use machine learning to predict the technical properties of fragrance
formulas. These learned models are then used to filter prospective formulas to remove those that will
not meet regulatory requirements.

3Conclusions

Fragrance creation is an area that is ripe for the application of AI because it requires deep technicalknowledge, the ability to predict human response to complex combinations of ingredients and theneed for originality. Our work is aimed at helping Perfumers be more creative and productive.

2References[1] Lynette Hamblin. Prospective; a scentimental education. New Scientist, 55:157, 7 1972.
[2] Lav R. Varshney, Florian Pinel, Kush R. Varshney, Angela Schörgendorfer, and Yi-Min Chee.
Cognition as a part of computational creativity. In IEEE 12th International Conference on
Cognitive Informatics and Cognitive Computing, ICCI*CC 2013, New York, NY, USA, July 16-18,
2013, pages 36–43, 2013.
[3] Yong yeol Ahn, Sebastian E. Ahnert, and James P. Bagrow. Flavor network and the principles offood pairing. Bulletin of the American Physical Society, 2011.
[4] Basenotes. http://www.basenotes.net/, 2017. Accessed November 1, 2017.
[5] Laurens Van Der Maaten and Geoffrey Hinton. Visualizing Data using t-SNE. Journal of
Machine Learning Research, 9:2579–2605, 2008.

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