On the Use of Weighted Mean Absolute Error in Recommender Systems.
Sergio Cleger‐Tamayo, Juan Manuel Fernández-Luna, Juan F. Huete · 2012
The classical strategy to evaluate the performance of a Recommender System is to measure the error in rating predictions. But when focusing on a particular dimension in a recommending process it is reasonable to assume that every prediction should not be treated equally, its importance depends on the degree to which the predicted item matches the deemed dimension or feature. In this paper we shall explore the use of weighted Mean Average Error (wMAE) as an alternative to capture and measure their effects on the recommendations. In order to illustrate our approach two different dimensions are considered, one item-dependent and the other that depends on the user preferences. 1.