Learning of user preferences, as represented by, for example, Conditional Preference
Networks (CP-nets), has become a core issue in AI research. Recent studies investigate …

A Qualitative Constraint Network (QCN) is a constraint graph representing problems under
qualitative temporal or spatial relations. More formally, a QCN includes a set of entities and a …

User's choices involve habitual behavior and genuine decision. Habitual behavior is often
expressed using preferences. In a multiattribute case, the Conditional Preference Network …

This paper focuses on graphical models for modelling preferences in combinatorial space
and their use for item optimisation. The preferential optimisation task seeks to find the …

CP-nets are a very expressive graphical model for representing preferences over
combinatorial spaces. They are particularly well suited for settings where an important task …

In this work, we present a novel human-in-the-loop framework to help the human user
understand the decision making process that involves choosing preferred options. We focus …

As a graphical model, Conditional Preference Networks (CP-nets) are used to describe the
qualitative conditional preferences of attributes, and the structure learning plays an …

Conditional preference statements have been used to compactly represent preferences over
combinatorial domains. They are at the core of CP-nets and their generalizations, and …

Preferences are very important in research fields such as decision making,
recommendersystemsandmarketing. The focus of this thesis is on preferences over …

There are many different recommender systems available in this technological era, each
with its own set of features and distinct selling points. We propose a new interactive system …