This tutorial, addressed to researchers and practitioners in the area of recommendation systems, reviews the foundations of decision and utility theory, focusing on methods for representing, learning and reasoning with preferences. Recent advances on handling preferences will also be discussed, with a focus on preference learning, preference aggregation and preferences in argumentation. Finally, we will show how these techniques can be used in a practice in recommender systems.

Usually, in argumentation, the proof-standards that are used are fixed a priori by the procedure. However (multicriteria) decision-aiding is a context where it may be modified dynamically during the process, depending on the responses of the decision-maker. The expert indeed needs to adapt and refine its choice of an appropriate method of aggregating arguments pros and cons, so that it fits the preference model inferred from the interaction. In this short paper we introduce how this aspect can be handled in an argumentation-based decision-aiding framework. The first contribution of the paper is conceptual: the notion of a concept lattice based on simple properties and allowing to navigate among the different proof-standards is put forward. We then show how this can be integrated within the Carneades model.

The article discusses the use of positive and negative reasons when preferences about alternative options have to be considered. Besides explaining the intuitive and formal situations where such a bipolar reasoning is used, the article shows how it is possible to generalize the concordance/discordance principle in preference aggregation and apply it to the problem of aggregating preferences expressed under intervals. © 2008 Wiley Periodicals, Inc.

Software entities (software products or processes) are characterized by many attributes, each one in its turn can be measured by one or more measures. In several cases the software entities have to be evaluated as a whole, thus raising the problem of aggregating measures to give an overall, single view on the software entity. This paper presents a method to aggregate measures, which works by comparing the product/process with predefined, ideal entities, or profiles. Profiles are defined starting from ranges of values on measures of attributes. The method is based on two main phases, namely definition of the evaluation model and application of the evaluation model. It is presented through a simplified case study that deals with evaluating the level of quality of an asset to decide whether to accept it in a reuse repository. A plausible way of how the method could be applied to process maturity assessment is also provided. The advantages of the method are that it allows using ordinal scales, while it deals explicitly with preferences expressed, implicitly or explicitly, by the evaluator.

Software artifacts are characterised by many attributes, each one in its turn can be measured by one or more measures. In several cases the software artifact has to be evaluated as a whole, thus raising the problem of aggregating measures to give an overall, single view on the artifact.This paper presents a method to aggregate measures, that works comparing the artifact with predefined, ideal artifacts, or profiles. Profiles are defined starting fromranges of values on measures of attributes. The method is based on two main phases, namely definition of the evaluation model and application of the evaluation model, and is presented in a simplified case study that deals with evaluating the level of quality of an asset to decide if accepting it in a reuse repository. The advantages of the method are that it allows using ordinal scales, while it deals explicitly with preferences expressed, implicitly or explicitly, by the evaluator.

The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the “missing value” semantics and the “absent value” semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory to handle these situations. The first generalisation introduces the use of a non symmetric similarity relation in order to formalise the idea of absent value semantics. The second proposal is based on the use of valued tolerance relations. A logical analysis and the computational experiments show that for the valued tolerance approach it is possible to obtain more informative approximations and decision rules than using the approach based on the simple tolerance relation.