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Building a recommendation system that is high-quality, high-performance, and hallucination-free can be a challenge. In this video, Yufeng Guo introduces Keras Recommenders (KerasRS), a library designed to help developers build reliable ranking and retrieval models with ease.
We’ll walk through a complete code example using the MovieLens dataset to build a Sequential Retrieval model. Using a Gated Recurrent Unit (GRU) to analyze a user’s watch history, we will predict exactly which movie they are likely to watch next.
Because KerasRS is built on Keras 3, this workflow is compatible with your choice of backend: TensorFlow, JAX, or PyTorch.
In this video, you will learn:
– What Keras Recommenders is and why it’s useful.
– How to prepare sequential data (using the “snake” method) for training.
– How to build a Two-Tower architecture with a Query Tower (GRU) and Candidate Tower.
– How to use the BruteForceRetrieval layer for accurate predictions.
Resources:
Build and train a recommender system in 10 minutes using Keras and JAX → https://goo.gle/3OKxUeI
Keras Recommenders Documentation → https://goo.gle/42yNQnl
Check out the Code Example → https://goo.gle/4n2by58
Chapters:
0:00 – Introduction: LLMs vs. Keras Recommenders
0:40 – What is KerasRS?
2:09 – Installation & Setup
3:04 – Sequential Retrieval & GRU Explained
4:30 – Preparing the MovieLens Dataset
6:35 – Data Batching & Structure
7:17 – Building the Two-Tower Model
8:14 – Making Movie Predictions
8:28 – Conclusion & Next Steps
Speakers: Yufeng Guo
Products Mentioned: Google AI Read More Google for Developers
