Recommender systems have become an important personalization technique
on the web and are widely used especially in e-commerce applications.
However, operators of web shops and other platforms are challenged by
the large variety of available algorithms and the multitude of their
possible parameterizations. Since the quality of the recommendations that are
given can have a significant business impact, the selection
of a recommender system should be made based on well-founded evaluation
data. The literature on recommender system evaluation offers a large
variety of evaluation metrics but provides little guidance on how to choose
among them. The paper which is presented in this presentation focuses on the often neglected aspect of clearly defining the goal of an evaluation and how this goal relates to the
selection of an appropriate metric. We discuss several well-known
accuracy metrics and analyze how these reflect different evaluation goals. Furthermore we present some less well-known metrics as well as a variation of the area under the curve measure that are particularly suitable for the evaluation of
recommender systems in e-commerce applications.