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

Sentiment analysis

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

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

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

LibQUAL+ model is the best-known method for the quality evaluation of library services, but it has two major drawbacks. First, to measure the quality, it is devised on a cardinal scale: the service levels range from 1 to 9. However, the standard representation of the concepts used by humans for communication is the natural language and, hence, users should express their judgments by using words instead of numbers. Second, it considers that all users’ opinions are equally important. Nevertheless, users do not play an equal role in assessing the service quality, i.e., the opinion given by some users should be more relevant than the opinion provided by others. To solve these drawbacks, we present an extended LibQUAL+ model representing the users’ perceptions by using a fuzzy linguistic modeling and taking into account that users’ opinions on the library services are not equally important.

A Fuzzy Linguistic Extended LibQUAL+ Model to Assess Service Quality in Academic Libraries

A fuzzy linguistic evaluation model of digital libraries based on the libqual+methodology

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