Deep Learning Recommender System

Retrieval and Ranking you own this product

This project is part of the liveProject series Real-World Deep Learning Recommender System
prerequisites
intermediate Python data science libraries • intermediate machine learning, intermediate recommender system experience (specifically Two Towers) • basics of developing an ML pipeline • intermediate TensorFlow 2.x
skills learned
split a single recommendation system to two essential parts, retrieval and ranking, using TensorFlow Recommenders • understand what Feature Stores are and how to use them
Shaked Zychlinski
1 week · 4-6 hours per week · ADVANCED
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liveProject This project is part of the liveProject series Real-World Deep Learning Recommender System liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.49 $29.99 you save $10 (35%)
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Real-world recommendation systems work with millions of users and billions of items. To handle this massive scale, recommendation systems tend to be divided into two types of models (retrieval and ranking), running one after the other, narrowing down the set of items each time. In this liveProject, you’ll implement a retrieval model using TensorFlow Recommenders, combine it with a ranking model, then create a fully functional pipeline going through both models, using a Feature Store. Finally, you’ll explore the scenario where you can’t (or choose not to) run both retrieval and ranking models online in real-time, leveraging the Feature Store once more to use the retrieval model offline.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Shaked Zychlinski

Shaked is currently leading the recommendation research group and company’s recommendations efforts at Lightricks, developing the company's RS algorithms from the ground up. Prior to this, he worked at and led projects at the Algo group of Taboola, one of the largest content recommendation companies in the world. He is a featured writer on Towards Data Science, with hundreds of reads each day. He has also developed the Dython library for Python, with 26k (and counting) downloads a month.

prerequisites

This liveProject is for data scientists with theoretical knowledge of machine learning, deep learning, and recommender systems who want to take the next step in their career. To begin these liveProjects you will need to be familiar with the following:


TOOLS
  • Intermediate Python (NumPy, pandas, Matplotlib)
  • Intermediate scikit-learn
  • Basics of TensorFlow 2.x (Keras interface)
  • TensorFlow Recommenders (retrieval and ranking models)
TECHNIQUES
  • Basic linear algebra (vectors, spaces, matrix transformations)
  • Define, train, and evaluate models

you will learn

In this liveProject, you’ll learn to create a fully functional pipeline going through both a retrieval and a ranking model, using a Feature Store:


  • Split a single recommendation system to two essential parts, retrieval and ranking, using TensorFlow Recommenders
  • Understand what Feature Stores are and how to use them

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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