Converting first-time users into recurring ones is key to the success of Web-based applications. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) to provide useful recommendations to users without historical data. Traditionally, RSs assume that non-personalized recommendation can mitigate this problem. However, several users are not interested in consuming just biased-items, such as popular or best-rated items. Then, we introduce two new approaches inspired by user coverage maximization to deal with this problem. These coverage-based RSs reached a high number of distinct first-time users. Thus, we proposed to compose the product’s page by mixing complementary non-personalized RSs. An online study, conducted with 204 real users confirmed that we should diversify the RSs used to conquer first-time users.