Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Wozniak%20-%20Multiple%20Classifier%20Systems%20-%202014.pdf

A survey of multiple classifier systems as hybrid systems

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1638_IF-2014-71-4-13.pdf

A review of soft consensus models in a fuzzy environment

https://sci2s.ugr.es/sites/default/files/files/ScientificImpact/2012-Martinez-INS.pdf

An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges

Sentiment analysis

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1466_2012-Porcel-INS.pdf

A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office

Sometimes, we find decision situations in which it is difficult to express some preferences by means of concrete preference degrees. In this paper, we present a consensus model for group decision making problems in which the experts use linguistic interval fuzzy preference relations to represent their preferences. This model is based on two consensus criteria, a consensus measure and a proximity measure, and on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process.

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1524_2012-EswA-Consensus.pdf

A consensus model for group decision making problems with linguistic interval fuzzy preference relations

An important question in group decision-making situations is how to estimate the consensus achieved within the group of decision makers. Dictionary meaning of consensus is a general and unanimous agreement among a group of individuals. However, most of the approaches deal with a more realistic situation of partial agreement. Defining a partial agreement of decision makers as a consensus up to some degree, the following question is how to obtain that soft degree of consensus. To do so, different approaches, in which the decision makers express their opinions by using symmetrical and uniformly distributed linguistic term sets, have been proposed. However, there exist situations in which the opinions are represented using unbalanced fuzzy linguistic term sets, in which the linguistic terms are not uniform and symmetrically distributed around the midterm. The aim of this paper was to study how to adapt the existing approaches obtaining soft consensus measures to handle group decision-making situations in which unbalanced fuzzy linguistic information is used. In addition, the advantages and drawbacks of these approaches are analyzed.

Soft consensus measures in group decision making using unbalanced fuzzy linguistic information

Industry 4.0: a perspective based on bibliometric analysis

Interval fuzzy preference relations can be useful to express decision makers' preferences in group decision-making problems. Usually, we apply a selection process and a consensus process to solve a group decision situation. In this paper, we present a consensus model for group decision-making problems with interval fuzzy preference relations. This model is based on two consensus criteria, a consensus measure and a proximity measure, and also on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process. We show an application example in social work.

/pdf/a-consensus-model-for-group-decision-making-problems-with-2yct5z7de7.pdf

M. A. Martínez

Papers

A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office

A consensus model for group decision making problems with linguistic interval fuzzy preference relations

Soft consensus measures in group decision making using unbalanced fuzzy linguistic information

Industry 4.0: a perspective based on bibliometric analysis

A consensus model for group decision-making problems with interval fuzzy preference relations