\section{Introduction}

Recommendation Systems (RSs) are among the most important tools used to help users in the decision-making process \cite{koren2009matrix}. These tools concern to satisfy properly the needs of existing users in several real domains. However, under the business perspective, a relevant scenario has been neglected by RSs: the adhesion of first-time users. Retaining these users in the system requires addressing issues such as: \textit{Considering a product catalog, which items have the highest potential to turn a given first-time user into a returning one?}; and \textit{How to recommend available items in order to retain the maximum number of first-time users?}. This problem is known in the literature in two ways: (1) Cold-Start problem; and (2) Ramp-up problem. The Cold-Start problem is related to generating recommendations for new users, whose consumption history is small and little relevant \cite{adomavicius2005toward, bobadilla2013recommender}. On the other hand, the Ramp-up problem is even more complicated, since it is related to first-time users, for whom there is still no information in the system \cite{rashid2002getting, martin2014}. In the absence of consumption histories, traditional strategies of Collaborative Filtering (CF) and Content-Based (CB) RSs are not able to provide personalized recommendation models and, consequently, they cannot issue good recommendations. Hence, the so-called non-personalized RSs emerge as the main recommendation strategies for this scenario.\looseness=-1

Non-personalized RSs are strategies that exploit metadata derived from the items and/or consumption in a domain, such as items popularity, best-rated items or consumer recurrence \cite{iskold2007art}. Such approaches are based on the premise that the consumption is generally biased towards one or more of those dimensions, allowing achieving high efficiency through simple and unsupervised prediction strategies. Although this assumption is valid for the ``mass consumption'', in which users consume items that emerge as a common interest of the majority, we cannot assume it for ``niche consumers'', who are interested in non-popular items. Therefore, straightforward recommendation of popular, recent or best-rated products for ``niche consumers'' may not represent an appropriate strategy. Properly identifying items that satisfy this user profile, however, is crucial for various scenarios, since these users may represent more than half of the profit on e-commerce systems, composing what is known as the long tail consumption \cite{anderson2006long}.\looseness=-1

In this study, we introduce a novel non-personalized recommendation strategy that best suits the needs of both mass and niche consumers. The proposed strategy is a practical application of the well-know problem of Maximum Coverage on recommendation scenarios \cite{michael1979computers}. The Maximum Coverage strategy aims to identify a subset of unrated items in a specific domain that can be potentially relevant to the largest number of different users. The premise, in this case, is that it is possible to recommend items that satisfy the largest number of users, bringing also more diversity when compared to strategies that just recommend the popular, recent or best rated items. Furthermore, the chances of a specific user to find at least one item that suits his/her personal needs increases since the strategy aims to cover a wide range of distinct preferences. It is important to note that recommending items for mass consumption users is considered an easy task, since we have available a large volume of information about them. On the other hand, recommending items to satisfy niche consumers remains as a challenge and has implications for several scenarios.\looseness=-1

In order to assess the practical relevance of the recommendations generated by the Maximum coverage strategy, we evaluate these recommendations considering four quality dimensions: accuracy, precision, recall and diversity. Further, we consider two  data collections from {\it MovieLens}, related to movie recommendation, and we compare our approach against three non-personalized strategies: (1) popularity; (2) best-rated; (3) recent items and (4) random popularity. Indeed, the results corroborate the main premise assumed in this paper. Applying the Maximum Coverage strategy tends to bring more diversity to domain users, and it is able to satisfy at least once 97\% of the users in each data collection. Recommendations generated by Maximum Coverage present, in average, 13\% of items that are not considered popular, whereas the other strategies recommended only popular items. Considering the trade-off between accuracy and diversity, our approach presents gains up to 13.5\% over the baseline strategies. Focusing the analysis on the top-5, 10 and 20 recommendation tasks, which correspond to classical tasks in real scenarios, our approach achieved gains up to 23.8\%, 18.5\% and 14.3\%, respectively. Besides these results, recommendations based on Maximum Coverage are comparable to those provided by the other strategies w.r.t. classical accuracy metrics.\looseness=-1 

\textbf{Contributions.} We present a new strategy focused on non-personalized recommendations based on Maximum Coverage that aims to retain mass and niche users. We also proposed and adopted a new evaluation methodology that considers the accuracy and diversity, reflecting the desirable real scenarios. We emphasize that the contributions of this study are particularly relevant for scenarios on which the adopted RSs are focused in recommend non-popular items that aim to reach users with different preferences. Finally, it is important to mention that we did not find in the literature studies that address the issues raised on the applicability of Maximum Coverage problem for the ramp-up problem.\looseness=-1

The remainder of this paper is organized as follows. In Section 2, we describe the main concepts hereby exploited, as well as some related work. Then, Section 3 presents the Maximum k-Coverage algorithm, adapted for the ramp-up problem. In Section 4, we present the experimental design used to evaluate the Maximum Coverage strategy. Later, in Section 5 we present the results related to the evaluation of the proposed strategy. Finally, we present the conclusions in Section 6.\looseness=-1
