New approaches for evaluation: correctness and freshness: Extended Abstract

Abstract

The main goal of a Recommender System is to suggest relevant items to users, although other utility dimensions -- such as diversity, novelty, confidence, possibility of providing explanations -- are often considered. In this work, we study two dimensions that have been neglected so far in the literature: coverage and temporal novelty. On the one hand, we present a family of metrics that combine precision and coverage in a principled manner (correctness); on the other hand, we provide a measure to account for how much a system is promoting fresh items in its recommendations (freshness). Empirical results show the usefulness of these new metrics to capture more nuances of the recommendation quality.