CSC 6711 - Recommendation Systems

• Access and interpret public recommendation data sets
• Differentiate implicit and explicit ratings data
• Describe how recommendation data are obtained from user-facing systems
• Describe and implement recommendation algorithms such as average ratings, k-nearest neighbors, and matrix factorization
• Compare and contrast the tradeoffs in memory usage, run times, and prediction performance of recommendation algorithms
• Design experiments to evaluate predictions from recommendation systems
• Describe and interpret several metrics for evaluating predictions and their tradeoffs
• Describe the stages (candidate generation, filtering, scoring, and ranking) of modern recommendation systems architectures
• Integrate multiple data storage and processing components to implement a cohesive recommendation service
• Write different types of software documentation (API, architecture, deployment, etc.) for different audiences (users of an API, operators, and developers)