Course Outcome

• Recall fundamental concepts, types, and applications of recommendation systems.
• Understand collaborative filtering, content-based filtering, and hybrid methods. 
• Implement basic recommendation models using Python. 
• Analyze the challenges like cold start, sparsity, and scalability. 
• Evaluate recommendation systems using metrics such as precision, recall, RMSE. 
• – Design hybrid recommendation systems for practical applications.