Recommender Systems Complete Course Beginner to Advanced - Basics of Recommender System: Offline Evaluation Techniques

The video tutorial discusses four main aspects of evaluation: task, dataset, evaluation metrics, and data partitioning. It explains how tasks can be evaluated using rating predictions and top-N recommendations. The tutorial also covers data representation using User-Item Rating Matrices (URM) and the importance of data partitioning in evaluation. The focus is on understanding how to effectively evaluate recommendation systems using these methods.

What are the four main aspects to consider in offline evaluation systems?

Explain how rating predictions can be used to evaluate a recommendation system.

Describe the process of top-N recommendations in offline evaluation.

What is a User Rating Matrix (URM) and how is it structured?

Differentiate between relevant and non-relevant ratings in a dataset.

How can unknown ratings affect the evaluation of a recommendation system?

What is data partitioning and why is it important in evaluation?