Recommender Systems: Evaluation and Metrics
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course
Skills you'll learn
We may earn a commission if you enroll through our links — it never affects the price you pay.
Similar courses
Vector Databases: An Introduction with Chroma DB
Vector databases provide the solid foundation required by large language models to deliver AI-powered similarity searches and recommendation systems for e-commerce recommendations, cybersecurity fraud detection, medical diagnostics, bioinformatics research, and other complex analysis tasks. Begin learning how to take advantage of the efficiencies vector databases offer in this introductory course by IBM. In this introductory microcourse, you'll build your knowledge of vector database fundamentals and explore the importance of vector databases in today’s data management landscape. Then, dive
All levels