books & projects by Kim Falk
AI Recommender Systems
Customers expect fast, accurate suggestions that are perfectly aligned to their preferences, even when they have limited historical data and provide minimal input. AI Recommender Systems gives you a guided tour to the new era of recommenders, powered by LLMs, agents, and context engineering. Author Kim Falk takes you from your first introduction to recommenders all the way to evaluation and production-ready systems. You'll explore established and cutting-edge frameworks and algorithms as you learn how to customize and improve their outputs to fit your specific needs.
Each recommender system is a unique blend of data engineering, performance optimization, and, now, also AI-led tools like agents and RAG. As a developer, you also need an efficient process to interpret the domain in which you’re working and to train your system to respond to actual customer demand. And you have to understand your algorithms—where they’re strong, where they’re blind, how to shape output to the constraints your business cares about, and how to evolve with changing needs.
AI Recommender Systems introduces the complete pipeline of components that make up a modern recommender system, from candidate generation and filtering to scoring and reranking, and describes where LLMs can help. You'll learn to read the signals hiding in your data, including content metadata and user behavior, and to evaluate recommenders against the specific use cases they need to support. Along the way, you’ll learn how to integrate custom prompts, retrieval-augmented generation, and agents into new designs as well as how to retrofit them into your existing systems.
Finally, because most recommender systems are customer-facing, performance is a key aspect of their success. You’ll learn how to handle the cold-start problems every real system eventually hits and how to put a recommender into production with the monitoring it needs.
Build an ML Recommender System
Recommender systems are one of the most popular and lucrative uses of machine learning, allowing businesses and organizations to give personalized suggestions to their customers.
In this liveProject, you’ll use common tools of the Python data ecosystem to design, build, and evaluate a movie recommendation model for the movie website. You’ll kick off your project by building simple genre charts, then work with existing movie rating data to implement personalized recommendations for each customer. When you’ve completed this hands-on and interesting project, you’ll have mastered a cornerstone technique of machine learning that’s in demand across companies and industries.
Practical Recommender Systems
Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you’ll see how to collect user data and produce personalized recommendations. You’ll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you’ll encounter as your site grows.
