Basic Recommender Systems
Learning Objectives
- Basic Concepts
- Distinguish the different input data of a Recommender System
- Classify the different families of Recommender System
- Distinguish between implicit and explicit user feedback
- Express the Global Effects formul
- Requirements and Evaluation
- Decide the most appropriate evaluation metric for the recommendation task
- Decide the most appropriate splitting strategy
- Distinguish between error and accuracy metrics
- Discuss the importance of beyond accuracy metrics
- Content-Based filtering
- Define a content-based recommender system
- Describe how to improve the different similarity functions
- Discuss the importance of weighting the attributes
- Describe advantages and disadvantages of Content-Based Filtering vs Collaborative Filtering
- Collaborative Filtering
- Select the most appropriate similarity function
- Describe the difference between item-based and user-based models
- Describe association rules based recommenders
- Decide the most appropriate approach based on implicit or explicit feedback
Basic Concepts
Taxonomy of Recommender Systems
- Algorithems
