• Value-Focused models, where a value system for aggregating the user preferences on the different criteria is constructed. In such approaches, marginal preferences upon each criterion are synthesized into a total value function, which is
usually called the utility function [33]. These approaches are often referred to
as multi-attribute utility theory (MAUT) approaches.
• Multi-Objective Optimization models, where criteria are expressed in the form
of multiple constraints of a multi-objective optimization problem. In such approaches, usually the goal is to find a Pareto optimal solution for the original optimization problem [102]. They are also sometimes referred to as multi-
objective mathematical programming methodologies.
• Outranking Relations models, where preferences are expressed as a system of
outranking relations between the items, thus allowing the expression of incomparability. In such approaches, all items are pair-wise compared to each other,
and preference relations are provided as relations “a is preferred to b”, “a and b
are equally preferable”, or “a is incomparable to b” [76].
• Preference Disaggregation models, where the preference model is derived by
analyzing past decisions. Such approaches are sometimes considered as a subcategory of other modeling categories mentioned above, since they try to infer a
preference model of a given form (e.g., value function or outranking relations)
from some given preferential structures that have led to particular decisions in
the past. Inferred preference models aim at producing decisions that are at least
identical to the examined past ones [30].
Methodologies from all categories can be used in order to create global preference models for recommender systems, depending on the selected decision problematic and the environment in which the recommender system is expected to operate.