[Week 06] - RS - Evaluation Metrics

The video discussed various evaluation metrics and methods for recommender systems, including data sparsity, classification measures, and different evaluation approaches.
Different domains require different metrics for evaluating recommender systems.
Success factors for recommender systems include accuracy, recall, user satisfaction, response time, and online conversion.
Research methods for evaluation include experiments and non-experimental observational studies.
Data sparsity is a challenge in recommender systems and can be calculated using the sparsity formula.
The meeting discussed the classification and accuracy measures for evaluating query results in a journal article database.
The speaker explained the concepts of true positive, false positive, false negative, and true negative in relation to rating predictions.
The meeting covered different evaluation approaches for recommendation systems, including MAE, MSE, precision, recall, F1 score, and others.
The speaker explained the process of splitting data into training and testing sets for evaluation purposes.