Measuring anti-relevance: a study on when recommendation algorithms produce bad suggestions.

Typically, performance of recommender systems has been measured focusing on the amount of relevant items recommended to the users. However, this perspective provides an incomplete view of an algorithm's quality, since it neglects the amount of negative recommendations by equating the unknown and negatively interacted items when computing ranking-based evaluation metrics. In this paper, we propose an evaluation framework where anti-relevance is seamlessly introduced in several ranking-based metrics; in this way, we obtain a different perspective on how recommenders behave and the type of suggestions they make. Based on our results, we observe that non-personalized approaches tend to return less bad recommendations than personalized ones, however the amount of unknown recommendations is also larger, which explains why the latter tend to suggest more relevant items. Our metrics based on anti-relevance also show the potential to discriminate between algorithms whose performance is very similar in terms of relevance.