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Special Issue "Context-Aware Computing and Smart Recommender Systems in the IoT, Volume II"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 3902

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Special Issue Editors

Dr. Marco Lombardi

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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

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Guest Editor

Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy
Interests: artificial intelligence; context awareness; situation awareness; IoT; big data
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Dr. Zilu Liang

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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

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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 the 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 2200 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 (3 papers)

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Research

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Open AccessArticle

A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition

and

Electronics 2023, 12(8), 1915; https://doi.org/10.3390/electronics12081915 - 18 Apr 2023

Cited by 2 | Viewed by 912

Abstract

Human activity recognition (HAR) is crucial to infer the activities of human beings, and to provide support in various aspects such as monitoring, alerting, and security. Distinct activities may possess similar movements that need to be further distinguished using contextual information. In this paper, we extract features for context-aware HAR using a convolutional neural network (CNN). Instead of a traditional CNN, a combined 3D-CNN, 2D-CNN, and 1D-CNN was designed to enhance the effectiveness of the feature extraction. Regarding the classification model, a weighted twin support vector machine (WTSVM) was used, which had advantages in reducing the computational cost in a high-dimensional environment compared to a traditional support vector machine. A performance evaluation showed that the proposed algorithm achieves an average training accuracy of 98.3% using 5-fold cross-validation. Ablation studies analyzed the contributions of the individual components of the 3D-CNN, the 2D-CNN, the 1D-CNN, the weighted samples of the SVM, and the twin strategy of solving two hyperplanes. The corresponding improvements in the average training accuracy of these five components were 6.27%, 4.13%, 2.40%, 2.29%, and 3.26%, respectively. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, Volume II)

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Open AccessArticle

Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

Xiangdong Xu

Krzysztof Przystupa

and

Orest Kochan

Electronics 2023, 12(4), 906; https://doi.org/10.3390/electronics12040906 - 10 Feb 2023

Viewed by 1055

Abstract

Social network information has been widely applied to traditional recommendations that have received significant attention in recent years. Most existing social recommendation models tend to use pairwise relationships to explore potential user preferences, but overlook the complexity of real-life interactions between users and the fact that user relationships may be higher order. These approaches also ignore the dynamic nature of friend influence, which leads the models to treat different friend influences equally in different ways. To address this, we propose a social recommendation algorithm that incorporates graph embedding and higher-order mutual information maximization based on the consideration of social consistency. Specifically, we use the graph attention model to build higher-order information among users for deeper mining of their behavioral patterns on the one hand; while on the other hand, it models user embedding based on the principle of social consistency to finally achieve finer-grained inference of user interests. In addition, to alleviate the problem of losing its own hierarchical information after fusing different levels of hypergraphs, we use self-supervised learning to construct auxiliary branches that fully enhance the rich information in the hypergraph. Experimental results conducted on two publicly available datasets show that the proposed model outperforms state-of-the-art methods. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, Volume II)

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Review

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Open AccessReview

Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review

Zilu Liang

Electronics 2022, 11(20), 3384; https://doi.org/10.3390/electronics11203384 - 19 Oct 2022

Viewed by 1241

Abstract

The practice of quantified-self sleep tracking has become increasingly common among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization and are incapable of providing actionable recommendations that are tailored to users’ physical, behavioral, and environmental context. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, Volume II)

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