\section{Related Work}

Turning first-time users into returning ones is a major problem for the success of e-commerce systems \cite{hinz2010impact, herlocker1999algorithmic, batet2012turist}. The literature in Recommender Systems defines this problem as: (1) Cold-Start problem; and (2) Ramp-up problem. The Ramp-up problem is commonly deemed as a variation of the Cold-Start problem~\cite{bobadilla2013recommender}. Despite being closely related, both problems should be addressed differently. Whereas the Cold-Start problem deals with users with small consumption histories (i.e., inactive or new users), in the Ramp-up there is no consumption information about the users (i.e., first-time users). For e-commerce systems, any information is better than none and, for this reason, Ramp-up is a major challenge for Recommender Systems. The challenge raises due to the absence of information about the consumption history of users, impairing or precluding the consolidation of effective models. We identified three main categories of RSs designed to deal with the ramp-up problem: (1) Interactive RSs; (2) Hybrid RSs; (3) Non-personalized RSs.\looseness=-1

A straightforward strategy to overcome this problem is to adopt the so-called Interactive RSs \cite{mahmood2007learning,he2016interactive}, wherein the system acquires further information about the users by applying small questionnaires before recommending the first items \cite{rashid2002getting}. In this direction, \textit{Zhou et. al} \cite{zhou2010solving} modified a matrix factorization method according to the responses provided by the users to a questionnaire. Such as search engines, the main drawback of this strategy is that the quality of the recommendations depends on the information provided by the users. Since users may struggle to properly define their needs in some domains, adopting Interactive RSs may become a challenge. The second strategy comprises using external information (e.g., social, demographic or personal information) about users to build preliminary profile models through hybrid strategies, such as \textit{CF-content based}, \textit{CF-demographic based} and \textit{CF-social based} \cite{bobadilla2013recommender,safoury2013exploiting,loh2009identifying}. However, this approach may be not valid for several e-commerce scenarios, where users are interested on buying/consuming items without providing any personal, social or demographic information.\looseness=-1

The third category of strategies includes non-personalized RSs, which recommend items based on global information derived from the available data or user behavior in the system~\cite{bobadilla2013recommender}. Hence, the issued recommendations are the same for all users \cite{schafer1999recommender}. Simplicity, generalization capability, domain independence and good performance are characteristics that make non-personalized RSs the main strategy to address the ramp-up problem in practical scenarios. Most of these RSs exploits one of three pieces of information: item popularity, best rated items or release date. Popularity-based RSs consist of a simple and intuitive strategy that always recommends the most popular items of a dataset, regardless the target user. Item popularity is estimated by the number of distinct users who have consumed each item in the past. This approach has proven to be effective in several domains \cite{bobadilla2013recommender}. Non-personalized RSs based on the best rated items generate a ranking of items ordered decreasingly by the mean rating received by each item in the system. The assumption of this approach is that the best evaluated items tend to interest many users~\cite{bobadilla2013recommender}. Finally, RSs based on release date present to users the items more recently added to the system. Despite being a simple strategy, this non-personalized recommendation strategy is very popular in real scenarios, since users normally appreciate recent items or novelties~\cite{adomavicius2005toward}.\looseness=-1

In general, existing non-personalized RSs rely on the premise that consumption is strongly skewed towards one of these dimensions in scenarios wherein RSs are applicable, allowing high predictive efficacy. However, such premise may be valid only for ``mass consumption", in which users consume items that arouse interest in the majority of a population. The same cannot be said about ``niche consumption", in which users are interested in items different from the common taste. Thus, recommending items belonging to mass consumption to potential niche consumers would not represent a proper strategy to retain the latter in the system. Given the relevance of niche users in real scenarios, this work proposes a novel non-personalized solution for the ramp-up problem able to retail both mass and niche consumers. The proposed strategy applies the Maximum Coverage in order to identify $k$ distinct items that maximize the coverage of users who have consumed at least one of these items.\looseness=-1

The use of Maximum Coverage in the recommendation task is not totally novel~\cite{hammar2013using,puthiya2016coverage}. In \cite{hammar2013using}, the authors proposed a method based on Maximum Coverage to maximize the chances of a customer to make purchases. In turn, \cite{puthiya2016coverage} models items as a complete similarity graph and applied the Maximum Coverage on it. The goal is to identify the $k$ items unknown by each user $u$ similar to the maximum number of items relevant to $u$. In this approach, the authors consider aims to enhance simultaneously diversity and utility. The present work differs from both ones due to two reasons: (1) we focus on the ramp-up problem, considering the main goal of retaining first-time users in the system while diversifying recommendations; and (2) we maximize the user coverage rather than the item one.\looseness=-1
