The aim of this paper is to study the integration of multiplicative preference relation as a preference representation structure in fuzzy multipurpose decision-making problems. Assuming fuzzy multipurpose decision-making problems under di4erent preference representation structures (ordering, utilities and fuzzy preference relations) and using the fuzzy preference relations as uniform representa...

Dyad ranking is a specific type of preference learning problem, namely the problem of learning a model that is capable of ranking a set of feature vector pairs, called dyads. In this paper a situation is considered where feature vectors consists of identifiers only. A new method based on learning latent-features is introduced for this purpose. The method is evaluated on synthetic data and is ap...

Nonmarket valuation is commonly applied to infer the preferences of individuals for restoration policies after an environmental disaster. A crucial issue in this task is to determine the appropriate lapse of time after which the valuation techniques should be applied. This study investigates the role of the emotional load in explaining the dynamic patterns of elicited preferences. The results s...

The quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers’ ratings are just a way to express their preferences about the products presented in the same testing session. Therefor...

Recommendation can be defined as the problem of selecting, among a set of items, those ones that are likely of interest to the user. In case of a group of users, recommendations should satisfy, as far as possible, the preferences of all the group members. In order to elicit the group preferences, we present two different mechanisms: the first one consists in a voting procedure whereas the secon...

Preference learning is an appealing approach for affective recognition. Instead of predicting the underlying emotional class of a sample, this framework relies on pairwise comparisons to rank-order the testing data according to an emotional dimension. This framework is relevant not only for continuous attributes such as arousal or valence, but also for categorical classes (e.g., is this sample ...

In chess, as well as many other domains, expert feedback is amply available in the form of annotated games. This feedback usually comes in the form of qualitative information because human annotators find it hard to determine precise utility values for game states. Therefore, it is more reasonable to use those annotations for a preference based learning setup, where it is not required to determ...

Purpose Making techniques PROMETHEE, direction of research in this emerging field. Design/Methodology/Approach: literature related to MCDM techniques PROMETHEE, ELECTRE, VIKOR and TOPSIS. Literature has been segregated to understand various issues and work related to MCDM techniques PROMETHEE, ELECTRE, VIKOR and TOPSIS. A detailed review is used to sort out the literature and develop the resear...

Preference-based search is the problem of finding an item that matches best with a user’s preferences. User studies show that example-based tools for preference-based search can achieve significantly higher accuracy when they are complemented with suggestions chosen to inform users about the available choices. We discuss the problem of eliciting preferences in example-based tools and present th...

While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decision-theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment...