Introduction to Recommender Systems

• Definition and significance
• Historical context and evolution
• Applications in different industries (e-commerce, streaming services, social media)

Types of Recommender Systems

• Content-based filtering: Recommending items based on the properties of the items and a user profile.
• Collaborative filtering: Recommending items by identifying patterns of preferences among users.
• User-based
• Item-based
• Hybrid systems: Combining both methods to leverage their strengths.

Basic Algorithms and Metrics

• Overview of key algorithms (nearest neighbors, matrix factorization)
• Evaluation metrics (precision, recall, RMSE, MAE)

Challenges and Limitations

• Cold start problem
• Data sparsity
• Scalability

Case Study Discussion