  Providing recommendations that are both relevant and diverse is a key
consideration of modern recommender systems. Optimizing both of these measures
presents a fundamental trade-off, as higher diversity typically comes at the
cost of relevance, resulting in lower user engagement. Existing recommendation
algorithms try to resolve this trade-off by combining the two measures,
relevance and diversity, into one aim and then seeking recommendations that
optimize the combined objective, for a given number of items to recommend.
Traditional approaches, however, do not consider the user interaction with the
recommended items.
  In this paper, we put the user at the central stage, and build on the
interplay between relevance, diversity, and user behavior. In contrast to
applications where the goal is solely to maximize engagement, we focus on
scenarios aiming at maximizing the total amount of knowledge encountered by the
user. We use diversity as a surrogate of the amount of knowledge obtained by
the user while interacting with the system, and we seek to maximize diversity.
We propose a probabilistic user-behavior model in which users keep interacting
with the recommender system as long as they receive relevant recommendations,
but they may stop if the relevance of the recommended items drops. Thus, for a
recommender system to achieve a high-diversity measure, it will need to produce
recommendations that are both relevant and diverse.
  Finally, we propose a novel recommendation strategy that combines relevance
and diversity by a copula function. We conduct an extensive evaluation of the
proposed methodology over multiple datasets, and we show that our strategy
outperforms several state-of-the-art competitors. Our implementation is
publicly available at https://github.com/EricaCoppolillo/EXPLORE.
