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Context-Aware Computing and Smart Recommender Systems in the IoT, 2nd Edition

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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: closed (31 March 2025) | Viewed by 19480

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Special Issue Editors

Dr. Marco Lombardi

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Guest Editor

Department of Cultural Heritage Sciences (DISPAC), University of Salerno, Fisciano, SA, Italy
Interests: context-aware computing; recommender systems; internet of things; big data; knowledge management
Special Issues, Collections and Topics in MDPI journals

Dr. Domenico Santaniello

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Guest Editor

Department of Cultural Heritage Sciences (DISPAC), University of Salerno, Fisciano, SA, Italy
Interests: machine learning; artificial intelligence; context awareness; situation awareness; internet of things; big data
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Dr. Zilu Liang

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Guest Editor

Faculty of Engineering, Kyoto University of Advanced Science, Kyoto, Japan
Interests: ubiquitous computing; wearable computing; personal informatics; digital health
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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.

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Keywords

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

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Related Special Issue

Context-Aware Computing and Smart Recommender Systems in the IoT, 3rd Edition in Electronics

Published Papers (8 papers)

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Research

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24 pages, 623 KiB

Open AccessArticle

Job Recommendations: Benchmarking of Collaborative Filtering Methods for Classifieds

by Robert Kwieciński, Tomasz Górecki, Agata Filipowska and Viacheslav Dubrov

Electronics 2024, 13(15), 3049; https://doi.org/10.3390/electronics13153049 - 1 Aug 2024

Cited by 2 | Viewed by 1641

Abstract

Classifieds pose numerous challenges for recommendation methods, including the temporary visibility of ads, the anonymity of most users, and the fact that typically only one user can consume an advertised item. In this work, we address these challenges by choosing models and evaluation procedures that are considered accurate, diverse, and efficient (in terms of memory and time consumption during training and prediction). This paper aims to benchmark various recommendation methods for job classifieds, using OLX Jobs as an example, to enhance the conversion rate of advertisements and user satisfaction. In our research, we implement scalable methods and represent different approaches to the recommendations: Alternating Least Square (ALS), LightFM, Prod2Vec, RP3Beta, and Sparse Linear Methods (SLIM). We conducted A/B tests by sending millions of messages with recommendations to perform online evaluations of selected methods. In addition, we have published the dataset created for our research. To the best of our knowledge, this is the first dataset of its kind. It contains 65,502,201 events performed on OLX Jobs by 3,295,942 users who interacted with (displayed, replied to, or bookmarked) 185,395 job ads over two weeks in 2020. We demonstrate that RP3Beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also show that sending messages with recommendations generated by the ALS and RP3Beta models increases the number of users contacting advertisers. Additionally, RP3Beta had a 20% more significant impact on this metric than ALS. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, 2nd Edition)

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26 pages, 2611 KiB

Open AccessArticle

Applying “Two Heads Are Better Than One” Human Intelligence to Develop Self-Adaptive Algorithms for Ridesharing Recommendation Systems

by Fu-Shiung Hsieh

Electronics 2024, 13(12), 2241; https://doi.org/10.3390/electronics13122241 - 7 Jun 2024

Cited by 2 | Viewed by 1192

Abstract

Human beings have created numerous laws, sayings and proverbs that still influence behaviors and decision-making processes of people. Some of the laws, sayings or proverbs are used by people to understand the phenomena that may take place in daily life. For example, Murphy’s law states that “Anything that can go wrong will go wrong.” Murphy’s law is helpful for project planning with analysis and the consideration of risk. Similar to Murphy’s law, the old saying “Two heads are better than one” also influences the determination of the ways for people to get jobs done effectively. Although the old saying “Two heads are better than one” has been extensively discussed in different contexts, there is a lack of studies about whether this saying is valid and can be applied in evolutionary computation. Evolutionary computation is an important optimization approach in artificial intelligence. In this paper, we attempt to study the validity of this saying in the context of evolutionary computation approach to the decision making of ridesharing systems with trust constraints. We study the validity of the saying “Two heads are better than one” by developing a series of self-adaptive evolutionary algorithms for solving the optimization problem of ridesharing systems with trust constraints based on the saying, conducting several series of experiments and comparing the effectiveness of these self-adaptive evolutionary algorithms. The new finding is that the old saying “Two heads are better than one” is valid in most cases and hence can be applied to facilitate the development of effective self-adaptive evolutionary algorithms. Our new finding paves the way for developing a better evolutionary computation approach for ridesharing recommendation systems based on sayings created by human beings or human intelligence. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, 2nd Edition)

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13 pages, 5672 KiB

Open AccessArticle

Gradient-Based Optimization for Intent Conflict Resolution

by Idris Cinemre, Kashif Mehmood, Katina Kralevska and Toktam Mahmoodi

Electronics 2024, 13(5), 864; https://doi.org/10.3390/electronics13050864 - 23 Feb 2024

Viewed by 1400

Abstract

The evolving landscape of network systems necessitates automated tools for streamlined management and configuration. Intent-driven networking (IDN) has emerged as a promising solution for autonomous network management by prioritizing declaratively defined desired outcomes over traditional manual configurations without specifying the implementation details. This paradigm shift towards flexibility, agility, and simplification in network management is particularly crucial in addressing inefficiencies and high costs linked to manual management, notably in the radio access part. This paper explores the concurrent operation of multiple intents, acknowledging the potential for conflicts, and proposes an innovative reformulation of these conflicts to enhance network administration effectiveness. Following the initial detection of conflicts among intents using a gradient-based approach, our work employs the Multiple Gradient Descent Algorithm (MGDA) to minimize all loss functions assigned to each intent simultaneously. In response to the challenge posed by the absence of a closed-form representation for each key performance indicator in a dynamic environment for computing gradient descent, the Stochastic Perturbation Stochastic Approximation (SPSA) is integrated into the MGDA algorithm. The proposed method undergoes initial testing using a commonly employed toy example in the literature before being simulated for conflict scenarios within a mobile network using the ns3 network simulator. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, 2nd Edition)

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29 pages, 5397 KiB

Open AccessArticle

Comparison of a Hybrid Firefly–Particle Swarm Optimization Algorithm with Six Hybrid Firefly–Differential Evolution Algorithms and an Effective Cost-Saving Allocation Method for Ridesharing Recommendation Systems

by Fu-Shiung Hsieh

Electronics 2024, 13(2), 324; https://doi.org/10.3390/electronics13020324 - 11 Jan 2024

Cited by 10 | Viewed by 1818

Abstract

The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: “Not all marriages have happy endings”. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, 2nd Edition)

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21 pages, 4576 KiB

Open AccessArticle

Exploring Explainable Artificial Intelligence Techniques for Interpretable Neural Networks in Traffic Sign Recognition Systems

by Muneeb A. Khan and Heemin Park

Electronics 2024, 13(2), 306; https://doi.org/10.3390/electronics13020306 - 10 Jan 2024

Cited by 6 | Viewed by 2996

Abstract

Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to improve road safety and optimize traffic management. While existing TSR models perform well in challenging scenarios, their lack of transparency and interpretability hinders reliability, trustworthiness, validation, and bias identification. To address this issue, we propose a Convolutional Neural Network (CNN)-based model for TSR and evaluate its performance on three benchmark datasets: German Traffic Sign Recognition Benchmark (GTSRB), Indian Traffic Sign Dataset (ITSD), and Belgian Traffic Sign Dataset (BTSD). The proposed model achieves an accuracy of 98.85% on GTSRB, 94.73% on ITSD, and 92.69% on BTSD, outperforming several state-of-the-art frameworks, such as VGG19, VGG16, ResNet50V2, MobileNetV2, DenseNet121, DenseNet201, NASNetMobile, and EfficientNet, while also providing faster training and response times. We further enhance our model by incorporating explainable AI (XAI) techniques, specifically, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), providing clear insights of the proposed model decision-making process. This integration allows the extension of our TSR model to various engineering domains, including autonomous vehicles, advanced driver assistance systems (ADAS), and smart traffic control systems. The practical implementation of our model ensures real-time, accurate recognition of traffic signs, thus optimizing traffic flow and minimizing accident risks. Full article

(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, 2nd Edition)

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15 pages, 1158 KiB

Open AccessArticle

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

by Kwok Tai Chui, Brij B. Gupta, Miguel Torres-Ruiz, Varsha Arya, Wadee Alhalabi and Ikhlas Fuad Zamzami

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

Cited by 12 | Viewed by 2733

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, 2nd Edition)

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19 pages, 1356 KiB

Open AccessArticle

Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

by Xiangdong Xu, Krzysztof Przystupa and Orest Kochan

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

Cited by 9 | Viewed by 2707

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, 2nd Edition)

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Review

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26 pages, 563 KiB

Open AccessReview

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

by Zilu Liang

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

Cited by 4 | Viewed by 3232

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, 2nd Edition)

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