The document describes a perfume recommender system that was created to improve the online perfume shopping experience. It scrapes perfume reviews, notes, longevity, and sillage data from various websites to build word embeddings of fragrance notes. The recommender system uses these embeddings to suggest perfumes based on a user's preferences for specific notes like citrus or peach. The goal of the system is to potentially launch an online perfume shopping platform that helps users more easily find scents they will enjoy.

1. Perfume
Recommender
Mengwei Li
Insight Data Science

2. Context
● Choosing a perfume can be difficult. There are tens of thousands of
perfume products with hundreds of notes that characterize scent.
● You can only try 2 or 3 products before your nose “saturates”!

3. Context
● Online perfume shopping is also challenging.
● Little detail about the scent is provided by the vendor.
● A perfume recommender based on user reviews can improve online shopping
experience and boost sales.

4. Perfume Recommender
To the rescue!
http://app-demo.club

5. Scraped ratings & reviews from makeupalley.com
Scraped notes, longevity and sillage from fragrantica.com
Data

6. Meaningful embedding
Word2vec
Smells like citrus and peach
150,000 reviews tokenized, lemmatized
vocabulary covering 230 fragrance notes
It smells like citrus and peach
Embedding

7. Work Flow
Review texts
Cleaning
Tokenizing
Lemmatizing
Word2Vec
Embedding
Main Notes
Recommender

8. Outcome
● A perfume recommender based on word embedding on fragrance notes.
● Potential to become an online perfume shopping platform.

9. Mengwei Li
Ph.D. Chemical Engineering
Tufts University
B.E. Chemical Engineering
Zhejiang University