Diversity-oriented models have been developed to recommend top-K items, which utilize some static parameters to make a trade-off or construct an objective function, derived from item relevance and item diversity. However, such a process directly narrows the interest points of the item list, and would not satisfy users' preferences very much. Besides, the static parameters mentioned above make recommendation algorithms a lack of adaptability and limit their application scenarios. Aiming at improving the adaptability and efficiency of diversity-aware recommendations, we propose a coverage-based approach according to the concepts of user-coverage and users' interest domain we have defined in this paper. Our method is parameter-free and suitable for either implicit data or explicit data. From a technique perspective, we design an improved greedy algorithm, which is used to achieve user interest domain coverage maximization, and provide solid theoretical proof about performance guarantee on efficiency and recommendation quality. During the experiments, we compare our model against two novel methods on two real-world data sets. Experimental results demonstrate the superiority of our method over the state-of-the-art techniques in terms of item relevance and diversity.