Deep Learning Recommender System

Feedback-Loop and Exploration Methods 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
rerank a list of items after a model already predicted their rankings • add noise to the model to affect predictions as they’re made • combine lists with different rankings to increase traffic to lower ranking items • use a Feature Store as part of a prediction pipeline
Shaked Zychlinski
1 week · 4-6 hours per week · ADVANCED
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Look inside

Since recommender systems train and learn over the data they recommended themselves, they will never train over, learn, or recommend items that they didn’t already recommend for some reason, such as insufficient ranking to be seen by the user. It’s important to break this Feedback Loop in order to ensure that suitable recommendations aren’t missed. But you must strike a balance between deviating (just enough) from the system’s predictions through exploration and not defeating the system’s purpose altogether. In this liveProject, you’ll learn three methods of exploration that help you provide better recommendations to your users, as well as the costs and benefits of each.

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

video resources

When you start your liveProject, you get full access to the following videos 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 use exploration to improve your system’s recommendations:


  • Use sampling techniques as exploration methods post-predictions
  • Add noise to the model, affecting predictions as they’re made
  • Combine lists of less-seen movies with top-rated movies in order to increase traffic to the less-seen movies
  • Use a Feature Store as part of a prediction pipeline

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