Skyline queries are generally considered as a promising technique for mitigating the challenges posed by the ever growing amount of available information. However, despite nearly a decade of re-search, the application of the Skyline paradigm in real-world in-formation systems still failed to succeed. This fact was mainly attributed to two major problems: the poor performance of the employed algorithms and the hardly convincing usefulness of Sky-line sets as personalized and manageable query results. While most performance issues have nowadays been solved, the semantic issues of the result sets still remain: skyline sets are usually far too large to be manageable and show a very low degree of focus with respect to actual user preferences. This problem is mainly a result of the fairness of the underlying Pareto semantics used by Skyline queries: they provide no means to compensate across different attributes which results in inferior result quality. This paper summarizes the recent efforts in overcoming these semantic shortcoming by introducing the natural and intuitive concept of preference trade-offs to Skyline queries which provides a coopera-tive user interaction for further focusing and improving the se-mantic quality of skyline result sets.