Improving argumentation-based recommender systems through context-adaptable selection criteria

Recommender Systems based on argumentation represent an important proposal where the recommendation is supported by qualitative information. In these systems, the role of the comparison criterion used to decide between competing arguments is paramount and the possibility of using the most appropriate for a given domain becomes a central issue; therefore, an argumentative recommender system that offers an interchangeable argument comparison criterion provides a significant ability that can be exploited by the user. However, in most of current recommender systems, the argument comparison criterion is either fixed, or codified within the arguments. In this work we propose a formalization of context-adaptable selection criteria that enhances the argumentative reasoning mechanism. Thus, we do not propose of a new type of recommender system; instead we present a mechanism that expand the capabilities of existing argumentation-based recommender systems. More precisely, our proposal is to provide a way of specifying how to select and use the most appropriate argument comparison criterion effecting the selection on the user´s preferences, giving the possibility of programming, by the use of conditional expressions, which argument preference criterion has to be used in each particular situation.Fil: Teze, Juan Carlos Lionel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

Specification and Verification of Context-dependent Services

Current approaches for the discovery, specification, and provision of services ignore the relationship between the service contract and the conditions in which the service can guarantee its contract. Moreover, they do not use formal methods for specifying services, contracts, and compositions. Without a formal basis it is not possible to justify through formal verification the correctness conditions for service compositions and the satisfaction of contractual obligations in service provisions. We remedy this situation in this paper. We present a formal definition of services with context-dependent contracts. We define a composition theory of services with context-dependent contracts taking into consideration functional, nonfunctional, legal and contextual information. Finally, we present a formal verification approach that transforms the formal specification of service composition into extended timed automata that can be verified using the model checking tool UPPAAL.Comment: In Proceedings WWV 2011, arXiv:1108.208

A recovery operator for nontransitive approaches

In some recent articles, Cobreros, Egré, Ripley, & van Rooij have defended the idea that abandoning transitivity may lead to a solution to the trouble caused by semantic paradoxes. For that purpose, they develop the Strict-Tolerant approach, which leads them to entertain a nontransitive theory of truth, where the structural rule of Cut is not generally valid. However, that Cut fails in general in the target theory of truth does not mean that there are not certain safe instances of Cut involving semantic notions. In this article we intend to meet the challenge of answering how to regain all the safe instances of Cut, in the language of the theory, making essential use of a unary recovery operator. To fulfill this goal, we will work within the so-called Goodship Project, which suggests that in order to have nontrivial naïve theories it is sufficient to formulate the corresponding self-referential sentences with suitable biconditionals. Nevertheless, a secondary aim of this article is to propose a novel way to carry this project out, showing that the biconditionals in question can be totally classical. In the context of this article, these biconditionals will be essentially used in expressing the self-referential sentences and, thus, as a collateral result of our work we will prove that none of the recoveries expected of the target theory can be nontrivially achieved if self-reference is expressed through identities

Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

Overcoming data sparsity

Unilever is currently designing and testing recommendation algorithms that would make recommendations about products to online customers given the customer ID and the current content of their basket. Unilever collected a large amount of purchasing data that demonstrates that most of the items (around 80%) are purchased infrequently and account for 20% of the data while frequently purchased items account for 80% of the data. Therefore, the data is sparse, skewed and demonstrates a long tail. Attempts to incorporate the data from the long tail, so far have proved difficult and current Unilever recommendation systems do not incorporate the information about infrequently purchased items. At the same time, these items are more indicative of customers' preferences and Unilever would like to make recommendations from/about these items, i.e. give a rank ordering of available products in real time. Study Group suggested to use the approach of bipartite networks to construct a similarity matrix that would allow the recommendation scores for different products to be computed. Given a current basket and a customer ID, this approach gives recommendation scores for each available item and recommends the item with the highest score that is not already in the basket. The similarity matrix can be computed offline, while recommendation score calculations can be performed live. This report contains the summary of Study Group findings together with the insights into properties of the similarity matrix and other related issues, such as recommendation for the data collection

Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.Comment: Workshop on Theory of Belief Functions, Brest: France (2010