One of the key tasks in data mining and information retrieval is to learn preference relations between objects. Approaches reported in the literature mainly aim at learning preference relations between objects represented according to the classical attribute-value representation. However, the growing interest in data mining techniques able to directly mine data represented according to more sop...

Researchers, using contingent valuation (CV) to value changes in nonmarket goods, typically believe respondents always answer questions truthfully or they answer truthfully only when it is in their interest to do so. The second position, while consistent with economic theory, implies that interpreting survey responses depends critically on the incentive structure provided. We derive simple test...

We consider the problem of learning a user’s ordinal preferences on a multiattribute domain, assuming that her preferences are lexicographic. We introduce a general graphical representation called LP-trees which captures various natural classes of such preference relations, depending on whether the importance order between attributes and/or the local preferences on the domain of each attribute ...

In decision-making, information is usually provided by means of fuzzy preference relations. However, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved, and thus their fuzzy preference relations may be incomplete, i.e. some values may not be given or may be missing. In this paper we present a procedure to find out the missing values of an incomplet...

There is growing interest in discrete-choice experiments (DCE) as a method to elicit consumers' preferences in the health care sector. Increasingly this method is used to determine willingness-to-pay (WTP) for health-related goods. However, its external validity in the health care domain has not been investigated until today. This paper examines the external validity of DCE concerning the reduc...

One of the most challenging tasks in the development of recommender systems is the design of techniques that can infer the preferences of users through the observation of their actions. Those preferences are essential to obtain a satisfactory accuracy in the recommendations. Preference learning is especially difficult when attributes of different kinds (numeric or linguistic) intervene in the p...

The value of travel time savings (VTTS) is the monetary value attached to save a determined amount of travel time. VTTS is also the most important benefit category aimed at justifying investments in transport infrastructures by public administrations. Hence VTTS played a significant role in various economic studies, both analytical and empirical (Zamparini & Reggiani, 2007). "It is difficult to...

Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn ...

In a competitive environment, a stakeholder needs to be aware of the relative position of the alternative (for example, projects or products) in relation to those of their competitors, often accomplished through a comparison of alternatives. To demonstrate, a company knowing the chances of success of its proposed environmental project (Lahdelma, Salminen, & Hokkanen, 2000) and a manufacturer kn...

This paper reviews several recent multi-task learning algorithms in a general framework. Interestingly, the framework establishes a connection to recent collaborative filtering algorithms using lowrank matrix approximation. This connection suggests to build a more general nonparametric approach to collaborative preference learning that additionally explores the content features of items.