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Electronics
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Advances and Challenges of Recommender Systems in Smart City
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Advances and Challenges of Recommender Systems in Smart City

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 11512

Special Issue Editors

Dr. Ramón Carrasco

E-Mail Website
Guest Editor
Department of Marketing, Faculty of Statistics, Complutense University of Madrid, 28040 Madrid, Spain
Interests: multi-criteria decision-making models; sentiment analysis; recommender systems; data science applied to business, machine learning
Special Issues, Collections and Topics in MDPI journals
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. 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
Dr. Francisco Mata

E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: group decision making; consensus reaching processes; smart cities and citizen participation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological advances have made the management of cities increasingly more intelligent, allowing for automatic management of energy, air quality, noise monitoring, waste, traffic, the structural health of buildings, smart parking, smart lighting, the automation of public buildings, etc. In these areas it is often necessary to make a decision through a complex process in which several alternatives appear, especially from different digital services that have to be evaluated under different criteria, contexts, etc. Recommender systems are tools specialized in solving this type of issue, facilitating customized access to information and therefore helping in decision-making processes. Hence, the application of recommender systems in smart cities is particularly interesting, since on the one hand they facilitate a better proposal of services, and on the other hand, smart cities represent a very challenging scenario for the proper adaptation and improvement of recommender systems.

In this Special Issue on “Advances and Challenges of Recommender Systems in Smart City”, we intend to constitute a reference point in this research. Accordingly, we invite researchers to present their latest findings related to this subject.

Dr. Ramón Carrasco
Dr. Carlos Porcel
Dr. Jesús Serrano-Guerrero
Dr. Francisco Mata
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. Electronics 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

  • smart cities
  • recommender systems
  • decision-making
  • machine learning
  • deep learning

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Published Papers (4 papers)

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Research

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43 pages, 9034 KiB  
Article
Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities
by Andra Sandu, Liviu-Adrian Cotfas, Aurelia Stănescu and Camelia Delcea
Electronics 2024, 13(11), 2151; https://doi.org/10.3390/electronics13112151 - 31 May 2024
Cited by 10 | Viewed by 1069
Abstract
In recent years, the research community has increasingly embraced topics related to smart cities, recognizing their potential to enhance residents’ quality of life and create sustainable, efficient urban environments through the integration of diverse systems and services. Concurrently, recommender systems have demonstrated continued [...] Read more.
In recent years, the research community has increasingly embraced topics related to smart cities, recognizing their potential to enhance residents’ quality of life and create sustainable, efficient urban environments through the integration of diverse systems and services. Concurrently, recommender systems have demonstrated continued improvement in accuracy, delivering more precise recommendations for items or content and aiding users in decision-making processes. This paper explores the utilization of recommender systems in the context of smart cities by analyzing a dataset comprised of papers indexed in the ISI Web of Science database. Through bibliometric analysis, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors were extracted. The findings revealed an average annual scientific production growth of 25.85%. Additionally, an n-gram analysis across keywords, abstracts, titles, and keywords plus, along with a review of selected papers, enriched the analysis. The insights gained from these efforts offer valuable perspectives, contribute to identifying pertinent issues, and provide guidance on trends in this evolving field. The importance of recommender systems in the context of smart cities lies in their ability to enhance urban living by providing personalized and efficient recommendations, optimizing resource utilization, improving decision-making processes, and ultimately contributing to a more sustainable and intelligent urban environment. Full article
(This article belongs to the Special Issue Advances and Challenges of Recommender Systems in Smart City)
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14 pages, 1667 KiB  
Article
Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations
by Mashael Aldayel, Abeer Al-Nafjan, Waleed M. Al-Nuwaiser, Ghadeer Alrehaili and Ghadi Alyahya
Electronics 2023, 12(19), 4047; https://doi.org/10.3390/electronics12194047 - 27 Sep 2023
Cited by 9 | Viewed by 4584
Abstract
The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically [...] Read more.
The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such as the bilateral variational autoencoder (BiVAE) and light graph convolutional neural network, this research provides insights into the performance of different techniques in the context of tourism. The models were evaluated using a tourism dataset and assessed through a range of metrics. The results indicate that the BiVAE algorithm outperformed others in terms of ranking and prediction metrics, underscoring the significance of considering multiple measurements and exploring diverse techniques. The findings have practical implications for tourism marketers seeking to influence travel agents and offer valuable insights for researchers investigating this domain. Additionally, the proposed model holds potential for applications in travel recommendation systems, including attraction recommendations. Full article
(This article belongs to the Special Issue Advances and Challenges of Recommender Systems in Smart City)
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30 pages, 5819 KiB  
Article
Smart Cities and Citizen Adoption: Exploring Tourist Digital Maturity for Personalizing Recommendations
by Gabriel Marín Díaz, José Luis Galdón Salvador and José Javier Galán Hernández
Electronics 2023, 12(16), 3395; https://doi.org/10.3390/electronics12163395 - 10 Aug 2023
Cited by 4 | Viewed by 3003
Abstract
Due to the irruption of new technologies in cities such as mobile applications, geographic information systems, internet of things (IoT), Big Data, or artificial intelligence (AI), new approaches to citizen management are being developed. The primary goal is to adapt citizen services to [...] Read more.
Due to the irruption of new technologies in cities such as mobile applications, geographic information systems, internet of things (IoT), Big Data, or artificial intelligence (AI), new approaches to citizen management are being developed. The primary goal is to adapt citizen services to this evolving technological environment, thereby enhancing the overall urban experience. These new services can enable city governments and businesses to offer their citizens a truly immersive experience that facilitates their day-to-day lives and ultimately improves their standard of living. In this arena, it is important to emphasize that all investments in infrastructure and technological developments in Smart Cities will be wasted if the citizens for whom they have been created eventually do not use them for whatever reason. To avoid these kinds of problems, the citizens’ level of adaptation to the technologies should be evaluated. However, although much has been studied about new technological developments, studies to validate the actual impact and user acceptance of these technological models are much more limited. This work endeavors to address this deficiency by presenting a new model of personalized recommendations based in the technology acceptance model (TAM). To achieve the goal, this research introduces an assessment system for tourists’ digital maturity level (DMT) that combines a fuzzy 2-tuple linguistic model and the analytic hierarchy process (AHP). This approach aims to prioritize and personalize the connection and communication between tourists and Smart Cities based on the digital maturity level of the tourist. The results have shown a significant correlation between technology usage and the potential for personalized experiences in the context of tourism and Smart Cities. Full article
(This article belongs to the Special Issue Advances and Challenges of Recommender Systems in Smart City)
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Review

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22 pages, 3070 KiB  
Review
Emerging Perspectives on the Application of Recommender Systems in Smart Cities
by Gricela Andrade-Ruiz, Ramón-Alberto Carrasco, Carlos Porcel, Jesús Serrano-Guerrero, Francisco Mata and Mario Arias-Oliva
Electronics 2024, 13(7), 1249; https://doi.org/10.3390/electronics13071249 - 27 Mar 2024
Cited by 4 | Viewed by 1680
Abstract
Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this [...] Read more.
Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field. Full article
(This article belongs to the Special Issue Advances and Challenges of Recommender Systems in Smart City)
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