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Applied Sciences
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Soft-Computing-Based Decision Support Systems on the Web
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Soft-Computing-Based Decision Support Systems on the Web

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 5584

Special Issue Editors

Dr. Álvaro Tejeda Lorente

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, University of Granada, E-18071 Granada, Spain
Interests: recommender systems; knowledge discovery; social media; finance democratization
Dr. Carlos Porcel

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18071 Granada, Spain
Interests: recommender systems; fuzzy linguistic modeling; social media; personalized decision support systems
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Gabriel López Herrera

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
Interests: intelligent information retrieval; recommender systems; institutional repositories; soft computing; scientometry
Dr. Jesús Serrano-Guerrero

E-Mail Website
Guest Editor
Department of Information Technologies and Systems, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
Interests: recommender systems; information retrieval; soft computing; sentiment analysis; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current global Information Technology scenario, voluminous information from sources like webpages, blogs, social networks, or digital libraries, among others, is available to be processed and exploited. For this reason, new soft computing-based applications on the web are arising every day. Regarding this issue, it is necessary to deal with new problems in already known fields related to intelligent decision support systems such as multicriteria decision making applications, recommender systems, opinion mining, and information retrieval, among others.

This Special Issue on “Soft-Computing-Based Decision Support Systems on the Web” provides a forum to show original research works and real applications mainly related to possible uses of soft computing techniques applied to decision making, recommendation, decision support systems, and mechanisms for extracting, inferring, modeling, representing and handling information from heterogeneous sources available on the Internet.

Topics and themes can include but are not limited to:

  • Fuzzy multicriteria decision making on web applications
  • Fuzzy preference modeling in intelligent decision support systems
  • Soft computing based-information retrieval systems
  • Consensus analysis on social networks
  • Fuzzy social consensus and decision making
  • Intelligent decision-making systems based on soft computing for big data
  • Recommender systems based on fuzzy linguistic techniques
  • Applications of the multicriteria decision making and recommender systems: labor market intelligence, e-commerce, e-learning, or social media marketing
  • Analysis of reputation, credibility, and trust on social media data
  • Sentiment analysis/opinion mining in soft computing based-decision support systems
  • Aggregation of preferences based on soft computing

Dr. Álvaro Tejeda Lorente
Dr. Carlos Porcel
Dr. Antonio Gabriel López Herrera
Dr. Jesús Serrano-Guerrero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multicriteria decision making
  • Social networks
  • Recommender systems
  • Soft computing
  • Web applications
  • Decision support systems
  • Aggregation operators
  • Consensus analysis
  • Preference modelling

Published Papers (2 papers)

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Research

25 pages, 15047 KiB  
Article
Modelling the Degree of Emotional Concern: COVID-19 Response in Social Media
by Jose Moreno Ortega and Juan Bernabé-Moreno
Appl. Sci. 2021, 11(9), 3872; https://doi.org/10.3390/app11093872 - 25 Apr 2021
Cited by 4 | Viewed by 2534
Abstract
The massive impact caused by the COVID-19 pandemic has left no one indifferent, becoming an unprecedented challenge. The use of protections such as sanitary masks has become increasingly common, restrictions in our daily lives, such as social distancing or confinements, have had serious [...] Read more.
The massive impact caused by the COVID-19 pandemic has left no one indifferent, becoming an unprecedented challenge. The use of protections such as sanitary masks has become increasingly common, restrictions in our daily lives, such as social distancing or confinements, have had serious consequences on the economy and our welfare state. Although the measures imposed throughout the world follow the same pattern, they have been applied with different criteria depending on the country. Over extended periods of time, people tend to change their perception of an event and its magnitude, or in other words, they stop being so concerned despite the seriousness of the matter. In this paper, we introduce a new metric to quantify the degree of emotional concern of people being affected by a topic, and we confirm how populations from different countries follow this trend of downplaying the effect of the pandemic and reach a state of indifference. To do this, we propose a method to analyze the social media stream over time extracting the different emotional states from the Russel Circumplex plane and computing the shifting created by the tragic event—the pandemic. We complete this metric by incorporating searching behavior to reflect not only push contents but also pull inquiries. The resulting metric establishes a relationship between the pandemic and the emotional response by defining the degree of Emotional Concern. Although the method can be applied to any location with a significant and varied amount of geo-localized social media streams, the scope of this paper covers the most representative cities in Europe. Full article
(This article belongs to the Special Issue Soft-Computing-Based Decision Support Systems on the Web)
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18 pages, 557 KiB  
Article
Movie Recommendation through Multiple Bias Analysis
by Tae-Gyu Hwang and Sung Kwon Kim
Appl. Sci. 2021, 11(6), 2817; https://doi.org/10.3390/app11062817 - 22 Mar 2021
Cited by 1 | Viewed by 2329
Abstract
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method [...] Read more.
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users. Full article
(This article belongs to the Special Issue Soft-Computing-Based Decision Support Systems on the Web)
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