Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

The correct answer is C. Mean Absolute Error. The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library absolute values of the differences between forecast and the corresponding…

Model Evaluation and Selection

Question

Options

ACluster Density
BSupport Vector Count
CMean Absolute Error
DSum of Absolute Errors

How the community answered

(56 responses)

A
14% (8)
B
4% (2)
C
75% (42)
D
7% (4)

Explanation

The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average. The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing. Support vector count and cluster density do not apply to recommender systems. MAE and AUC are both valid and useful metrics for measuring recommender systems.

Topics

#recommender systems#evaluation metrics#Mean Absolute Error#model quality

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