Dora Petrella
How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)
#1about 5 minutes
Defining the business need for product recommendations
A recommendation system for substitute products is needed across multiple touchpoints to prevent lost sales from out-of-stock items.
#2about 2 minutes
Analyzing the limitations of the existing recommender
The previous system, based on the Jaccard coefficient, produced low-quality recommendations, particularly for new or unpopular items.
#3about 5 minutes
Using the Prod2Vec algorithm for recommendations
The Prod2Vec algorithm, adapted from Word2Vec, learns product relationships by analyzing co-occurrence within user session context windows.
#4about 2 minutes
Improving predictions with Meta-Prod2Vec and metadata
Incorporating product metadata like category and brand into the model (Meta-Prod2Vec) significantly improves recommendation quality for long-tail items.
#5about 2 minutes
Implementing the end-to-end MLOps pipeline
The production system uses dbt for data transformation, a Vertex AI pipeline for model training, and Elasticsearch for efficient vector similarity search.
#6about 3 minutes
Evaluating model performance with offline and online metrics
Offline metrics like NDCG confirmed model quality, while mirror traffic analysis showed a 45% increase in product recommendation coverage.
#7about 3 minutes
Visualizing product relationships with embedding projector
Using TensorFlow's Embedding Projector tool reveals how the model groups similar products into distinct clusters in a high-dimensional space.
#8about 3 minutes
Adopting pragmatic baselines and automated data analysis
Key project takeaways include using simple business-logic baselines for benchmarking and automating exploratory data analysis within the ML pipeline itself.
#9about 1 minute
Understanding the project team and final timeline
The project was completed in nine months by a cross-functional team of data engineers, data scientists, and software developers.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
02:56 MIN
Real-world examples of machine learning in e-commerce
Data Science in Retail
01:54 MIN
Real-world applications and key takeaways
Machine learning 101: Where to begin?
05:15 MIN
How AI powers e-commerce from logistics to discovery
Intelligence Everywhere: The Future of Consumer Tech
07:54 MIN
Demo of a unified model and business monitoring dashboard
Deployed ML models need your feedback too
02:19 MIN
Future ideas for personalized vacation planning
Hacking Your Vacation: Using Data for Fun
05:57 MIN
Adopting a holistic AI strategy across business functions
Fireside Chat with Werner Vogels, VP & CTO, Amazon.com & Daniel Gebler, CTO at Picnic
01:29 MIN
Overview of the data and machine learning tech stack
Empowering Retail Through Applied Machine Learning
10:46 MIN
Navigating the machine learning project lifecycle
Intelligent Automation using Machine Learning
Featured Partners
Related Videos
58:21Data Science in Retail
Julian Joseph
37:24Deployed ML models need your feedback too
David Mosen
21:01Hybrid AI: Next Generation Natural Language Processing
Jan Schweiger
26:54Make it simple, using generative AI to accelerate learning
Duan Lightfoot
29:24MLOps - What’s the deal behind it?
Nico Axtmann
48:01What non-automotive Machine Learning projects can learn from automotive Machine Learning projects
Jan Zawadzki
22:41Empowering Retail Through Applied Machine Learning
Christoph Fassbach & Daniel Rohr
35:16How AI Models Get Smarter
Ankit Patel
Related Articles
View all articles
.gif?w=240&auto=compress,format)
From learning to earning
Jobs that call for the skills explored in this talk.
Nomitri
Berlin, Germany
DevOps
Gitlab
Docker
Ansible
Grafana
+6
Hilo By Aktiia
Lausanne, Switzerland
Intermediate
Machine Learning