Introducing Keras Recommenders: state-of-the-art recommendation techniques at your fingertips

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