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Electronics
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Context-Aware Computing and Smart Recommender Systems in the IoT
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Context-Aware Computing and Smart Recommender Systems in the IoT

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 17919

Special Issue Editors

Dr. Marco Lombardi

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: context aware computing; recommender systems; knowledge management; IoT; big data
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Santaniello

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: artificial intelligence; context awareness; situation awareness; IoT; big data; cultural heritage preservation
Special Issues, Collections and Topics in MDPI journals
Dr. Zilu Liang

E-Mail Website
Guest Editor
Faculty of Engineering, Kyoto University of Advanced Science, Kyoto 621-8555, Japan
Interests: ubiquitous computing; wearable computing; human-computer interaction; data science; health informatics; machine learning; natural language processing; serious game; eye-tracking; fNIRS brain imaging; VR/XR/oculus
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Khan

E-Mail Website
Guest Editor
ComNets Lab, Department of Computer Science, New York University, Abu Dhabi 129188, United Arab Emirates
Interests: artificial intelligence; TCP congestion control; machi; self-organizing network; cloud radio access networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Context-aware computing describes the development of technologies and applications that can detect data from the surrounding environment and react accordingly with specific actions, reducing and simplifying the human–machine interaction process. The latter automatically offer a range of services to help the user during daily professional or private life by managing the available resources. Therefore, context awareness should be intended as a set of technical features able to provide added value to services in different application segments.

Context changes result in a transformation of the user experience. For this reason, context-aware computing has played a key role in addressing this challenge in previous paradigms, such as mobile and pervasive computing, and is playing a crucial role in the Internet of Things (IoT) paradigm. Indeed, thanks to new technologies, a user can access large amounts of content and services with different purposes and requirements in each context. In this scenario, the need arises for recommendation systems that consider users’ personal preferences and all the contextual aspects to recommend the right services and contents at a specific time.

This Special Issue aims to present studies in context-aware computing, recommender systems, and IoT. Researchers are invited to submit their manuscripts to this Special Issue and contribute their models, proposals, reviews, and studies.

Dr. Marco Lombardi
Dr. Domenico Santaniello
Dr. Zilu Liang
Dr. Muhammad Khan
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

  • context-aware computing
  • recommender systems
  • Internet of Things
  • smart environments
  • big data
  • ubiquitous computing
  • wearable computing
  • activity recognition

Published Papers (5 papers)

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Research

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20 pages, 869 KiB  
Article
A Serendipity-Oriented Personalized Trip Recommendation Model
by Rizwan Abbas, Ghassan Muslim Hassan, Muna Al-Razgan, Mingwei Zhang, Gehad Abdullah Amran, Ali Ahmed Al Bakhrani, Taha Alfakih, Hussein Al-Sanabani and Sk Md Mizanur Rahman
Electronics 2022, 11(10), 1660; https://doi.org/10.3390/electronics11101660 - 23 May 2022
Cited by 6 | Viewed by 2616
Abstract
Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a [...] Read more.
Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip. Full article
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT)
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19 pages, 1042 KiB  
Article
Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias
by Mario Casillo, Brij B. Gupta, Marco Lombardi, Angelo Lorusso, Domenico Santaniello and Carmine Valentino
Electronics 2022, 11(7), 1003; https://doi.org/10.3390/electronics11071003 - 24 Mar 2022
Cited by 20 | Viewed by 3040
Abstract
In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited [...] Read more.
In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising. Full article
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT)
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18 pages, 7569 KiB  
Article
Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins
by Alberto Berenguer, David Fernández Ros, Andrea Gómez-Oliva, Josep A. Ivars-Baidal, Antonio J. Jara, Jaime Laborda, Jose-Norberto Mazón and Angel Perles
Electronics 2022, 11(6), 835; https://doi.org/10.3390/electronics11060835 - 08 Mar 2022
Cited by 7 | Viewed by 3084
Abstract
Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a [...] Read more.
Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination. Full article
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT)
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16 pages, 4055 KiB  
Article
Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services
by Cach N. Dang, María N. Moreno-García and Fernando De la Prieta
Electronics 2021, 10(20), 2459; https://doi.org/10.3390/electronics10202459 - 10 Oct 2021
Cited by 9 | Viewed by 3434
Abstract
Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of media [...] Read more.
Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of media data can be exploited to improve their reliability. In the case of media social data, sentiment analysis of the opinions expressed by users, together with properties of the items they consume, can help gain a better understanding of their preferences. In this study, we present a recommendation approach that integrates sentiment analysis and genre-based similarity in collaborative filtering methods. The proposal involves the use of BERT for genre preprocessing and feature extraction, as well as hybrid deep learning models, for sentiment analysis of user reviews. The approach was evaluated on popular public movie datasets. The experimental results show that the proposed approach significantly improves the recommender system performance. Full article
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT)
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Review

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30 pages, 3205 KiB  
Review
Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review
by Álvaro Lozano Murciego, Diego M. Jiménez-Bravo, Adrián Valera Román, Juan F. De Paz Santana and María N. Moreno-García
Electronics 2021, 10(13), 1555; https://doi.org/10.3390/electronics10131555 - 27 Jun 2021
Cited by 13 | Viewed by 4045
Abstract
The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s [...] Read more.
The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specific time of day, the user’s physical activity, and geolocation, among many others. This context information is generally obtained by electronic devices used by the user to listen to music such as smartphones and other secondary devices such as wearables and Internet of Things (IoT) devices. The objective of this paper is to present a systematic literature review to analyze recent work to date in the field of context-aware recommender systems and specifically in the domain of music recommendation. This paper aims to analyze and classify the type of contextual information, the electronic devices used to collect it, the main outstanding challenges and the possible opportunities for future research directions. Full article
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT)
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