Offered By: IBM
Perfume Recommendation with Sentence-BERT
We meet people every day and making a memorable impression on others is not easy. Memories are heavily linked to our sense of smell. Thus a handpicked personal perfume can not only evoke our feelings of happiness and energy but also project a message of our unique identities to others we meet. As the notes of perfume will unfold over time, selecting the right one from different brands' collections by smelling is not effortless. Why not use Machine Learning to build a perfume recommender system? This guided project teaches you how to build such a system based on documented perfume notes.
Continue readingGuided Project
Machine Learning
189 EnrolledAt a Glance
We meet people every day and making a memorable impression on others is not easy. Memories are heavily linked to our sense of smell. Thus a handpicked personal perfume can not only evoke our feelings of happiness and energy but also project a message of our unique identities to others we meet. As the notes of perfume will unfold over time, selecting the right one from different brands' collections by smelling is not effortless. Why not use Machine Learning to build a perfume recommender system? This guided project teaches you how to build such a system based on documented perfume notes.
In this guided project, you will first learn about Sentence-BERT for transforming the notes of the perfumes into semantically meaningful sentence embeddings that can be compared in terms of similarity metrics such as the cosine-similarity. Then you will use the embeddings to build a recommender system that outputs similar types of perfumes, as per the requirements of your manager at the fragrance retail company!Â
A Look at the Project Ahead
After completing this guided project you will be able to:
- Describe the working mechanics of Sentence-BERT
- Compute sentence embeddings using Python's sentence-transformers framework
- Perform Semantic Textual Similarity (STS) analysis on sentence embeddings
- Build a perfume recommender systemÂ
What You'll Need
For this guided project, it's recommended that you have a basic understanding of the BERT framework (Bidirectional Encoder Representations from Transformers) and the Transformers network. It would also be helpful if you have prior experience working with the Pandas library for manipulating data frames in Python. We also used PCA (Principal Component Analysis) for the purpose of visualizing the embeddings of the perfume notes.
Frequently Asked Questions
Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.
What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.