Rating

Rating metrics are specifically designed for recommender systems that predict explicit user ratings (e.g., 1-5 stars). These metrics quantify the accuracy of these numerical predictions by comparing them to the actual user ratings.

API Reference

For class signatures and source code, see the Rating Metrics API Reference.

MAE

Mean Absolute Error (MAE). The average absolute difference between predicted and actual ratings.

\[ \text{MAE} = \frac{1}{N} \sum_{(u,i)} |\hat{r}_{ui} - r_{ui}| \]

For further details, please refer to this link.

evaluation:
    metrics: [MAE]

MSE

Mean Squared Error (MSE). The average of the squared differences between predicted and actual ratings.

\[ \text{MSE} = \frac{1}{N} \sum_{(u,i)} (\hat{r}_{ui} - r_{ui})^2 \]

For further details, please refer to this link.

evaluation:
    metrics: [MSE]

RMSE

Root Mean Squared Error (RMSE). The square root of the MSE, providing an error measure in the same units as the ratings.

\[ \text{RMSE} = \sqrt{\frac{1}{N} \sum_{(u,i)} (\hat{r}_{ui} - r_{ui})^2} \]

For further details, please refer to this link.

evaluation:
    metrics: [RMSE]