Pedro José Sánchez

Maintenance is considered a key issue in hypermedia systems. Common sense indicates that it might be possible to understand the web's content without any knowledge representation, but not to automatically manage and support it. Assuming that the development and maintenance of hypermedia systems is dynamic and evolutionary, this implies a continuous process of change throughout their life cycles. Here, we review a conceptual hypermedia system model, the Semantic and Evolutionary Model for HyPermedia systems, to maintain its consistency during its life cycle. This model makes a semantic representation of the information system, offering an architecture whose components are formally defined by using the underlying semantics. In this article, we focus on formalizing the building of hypermedia information systems. To do so, we propose a novel axiomatic approach based on the graph theory that ensures their consistency. The major objective of this contribution is to describe a computational and mathematical proof that determines the set of axioms that are valid to keep the integrity of the information system. These axioms are able to generate the combinations that are valid by means of logical deduction.

Decision making is a usual process for human beings in their daily life. The complexity of real world decision making problems imply the necessity of multiple points of view. Since experts may provide their assessments by using different domains according to their knowledge area and background. Different approaches have been introduced in the literature, nevertheless, none of them has considered the use of hesitant information. In this contribution, we propose an approach that manages hesitant heterogeneous information such as hesitant fuzzy sets and hesitant fuzzy linguistic term sets with other information as numerical, linguistic and interval-valued. A hesitant heterogeneous decision making model is also presented.

Purpose: Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge‐based recommendation model to provide good cold start recommendations.Design/methodology/approach: Hybridizing a collaborative system and a knowledge‐based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers' preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach.Findings: The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge‐based recommender systems improves the performance in those situations when collaborative models do not work properly.Research limitations/implications: Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations.Practical implications: A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants.Originality/value: Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.