Building Reliable Recommendation Systems with Keras Recommenders (KerasRS)

Introduction to Keras Recommenders

• 💡 While Large Language Models (LLMs) can be used for recommendations, they often suffer from hallucinations.
• 🎯 Keras Recommenders (KerasRS) is introduced as a Python library designed to build reliable and high-performing recommendation systems without these issues.
• 🌐 Recommendation systems are crucial for everyday online experiences, appearing as suggestions in shopping, social media, and video platforms.

KerasRS: Features and Setup

• 🧩 KerasRS provides building blocks for tasks like ranking and retrieval.
• ⚙️ Built on Keras 3, it offers compatibility with TensorFlow, JAX, or PyTorch backends.
• 📦 Installation is straightforward via pip: pip install keras-rs.
• 📚 The library includes numerous examples, available as web pages and Colab notebooks.

Sequential Retrieval with GRU

• 🚀 The video focuses on sequential retrieval, predicting the next item in a series.
• 🧠 A Gated Recurrent Unit (GRU), a type of recurrent neural network, is used to analyze user history and predict the next item.
• 🎬 An example using the MovieLens dataset demonstrates predicting the next movie a user will watch.

Data Preparation and Model Architecture

• 📊 The MovieLens dataset is prepared by creating sequences of movie reviews for each user, ordered by timestamp.
• 🐍 The "snake" method is used to generate multiple training examples from a single user sequence, where each subsequent example includes one more item.
• 🧪 The final review in each sequence is set aside for the test dataset to ensure it's not exposed during training.
• 🧱 The model features a two-tower architecture: a query tower using GRU for historical sequences and a candidate tower with embeddings for potential recommendations.
• 🔍 The BruteForceRetrieval layer is used to decode query embeddings and retrieve predictions from the candidate tower.

Training and Prediction

• 🛠️ After data preparation, the model is instantiated, compiled, and trained using standard Keras procedures.
• 🎬 Predictions can be made using test data or by inputting a custom sequence of favorite movies to evaluate the model's suggestions.
• ✨ KerasRS offers various other example architectures, such as the Deep and Cross Network and two-tower embedding models for retrieval and ranking.