Abstract
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. This paper 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.
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