\section{Conclusion \& Future Work}

In this work we present a new approach for the user ramp-up problem in recommendation research area. Basically, we analyze the hypothesis that present diversified recommendations, which are able to combine popular items (i.e., related to mass consumption) and non popular items (i.e., related to niche consumption) may satisfy different user profiles. In this sense, we propose to model the domain as a bipartite graph and, then, apply the Maximum $k$-Coverage strategy to return the $k$ items that interest the higher number of distinct users. Under these assumptions, we evaluate this strategy using actual datasets from MovieLens (i.e., ML-1M e ML-10M) and consolidate the results based on an evaluation methodology that is able to consider the desirable trade-off between utility and diversity in Recommender Systems (RSs). We compare our proposal with traditional strategies for ramp-up problem:  Popularity, Best Rated and Recent Items.

First, we analyze the intersection of the recommendation lists of each strategy and we note that Maximum Coverage strategy presents up to 36\% of distinct items from the all other strategies, indicating that the Maximum Coverage may introduce novelty and diversity to recommendations. Next, we evaluate the utility of the recommendation list of each strategy using the metrics Accuracy, Precision and Recall. We observe that the traditional strategies Popularity and Best Rated are more effective, since the consumption of the users has a bias for the mass consumption. However, evaluating the diversity of the recommendations, we conclude that Recent Items and Maximum Coverage strategies are better than the other ones. Adopting an evaluation that balances utility (i.e., Accuracy) and diversity, we observe that: (1) Despite Recent Items strategy presents high diversity, it is not useful to end users; (2) The Popularity and Best Rated strategies, which present high accuracy, are not able to diversify the set of presenting items, not satisfying users of niche consumers; and (3) The Maximum Coverage strategy presents the best results related to the trade-off between utility and diversity, satisfying 97\% of the users.  In sum, our approach, based on a greedy algorithm for Maximum Coverage, presents gains up to 13.5\% when compared with traditional strategies, with a cubic complexity, corresponding to a practical and scalable strategy for scenarios with millions of items and users.

As future work, in order to consolidate even more our results,  we intend to evaluate these strategies adopting new datasets on which it is possible to distinguish different user profiles present in real scenarios. Moreover, our goal is to propose a new approach, similar to the one present in this paper, for the user cold-start problem, on which the users are participatory and perform few evaluations of items.