Personalization of database queries requires a semantically rich, easy to handle and flexible preference model. Building on preferences as strict partial orders we provide a variety of intuitive base preference constructors for numerical and categorical data, including so-called d-parameters. As a novel semantic concept for complex preferences we introduce the notion of ‘substitutable values’ (SV-semantics), characterizing equally good values amongst indifferent values. Pareto and prioritized preference construction preserves strict partial orders, which instantly solves crucial well-known problems for preference queries. We can point out a new semantic-guided way to cope with the infamous flooding effect of query engines. Contrary to a wide-spread belief we can give evidence that the result sizes of Pareto or skyline queries not necessarily explode for multiple attributes. Moreover, we can show that known laws from preference relational algebra remain valid under SV-semantics. Since most of these laws rely on transitivity, preservation of strict partial order is essential to algebraically optimize complex preference queries. Similarly, well-known efficient evaluation algorithms for the preference selection operator rely on transitivity. In a nutshell, preference constructors with SV-semantics enable an intuitive and powerful personalization of database queries and at the same time are the key to efficient preference query evaluation.