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Computers
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Advances in Semantic Multimedia and Personalized Digital Content
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Advances in Semantic Multimedia and Personalized Digital Content

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (25 February 2026) | Viewed by 11914

Special Issue Editors

Dr. Phivos Mylonas

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Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Troussas

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: personalization; human–computer interaction; artificial intelligence
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Dr. Akrivi Krouska

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: social network analysis; multimedia applications; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Manolis Wallace

E-Mail Website
Guest Editor
ΓAΒLAB—Knowledge and Uncertainty Research Laboratory, University of the Peloponnese, 22100 Tripoli, Greece
Interests: cultural informatics; semantics; uncertainty
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cleo Sgouropoulou

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: software engineering; educational technology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will showcase cutting-edge research in semantic and social multimedia adaptation, personalization, and AI-driven content technologies. It will include selected papers from the 20th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2025), which will be held in Mystras, Greece, on November 27–28, 2025. However, this Special Issue is also open to original submissions that are not part of the conference.

With the exponential growth of digital content across multiple platforms, understanding and optimizing user interaction with multimedia is more critical than ever. Advances in artificial intelligence (AI), machine learning, knowledge graphs, natural language processing, and deep learning are driving new methodologies for semantic analysis, adaptive media, and personalized experiences. This Special Issue seeks to explore novel approaches to semantic multimedia analysis, user modeling, content personalization, and AI-driven media adaptation to enhance the accessibility, relevance, and effectiveness of digital content.

We invite high-quality contributions addressing theoretical advancements, innovative applications, and emerging challenges in this interdisciplinary field. Topics of interest include, but are not limited to, the following:

  • Semantic-driven multimedia content creation and annotation
    • AI-powered metadata extraction and semantic tagging
    • Knowledge graph integration for multimedia understanding
    • Automated generation of multimedia summaries and captions
  • Personalized user profiling and adaptive content delivery
    • Dynamic user modeling based on behavior and preferences
    • Adaptive media recommendations on streaming platforms
    • Emotion-aware multimedia adaptation and affective computing
  • Integration of AI into media adaptation
    • Deep learning models for video, image, and audio personalization
    • AI-driven storytelling and content generation
    • Hybrid AI–human approaches for interactive media experiences
  • Context-aware multimedia applications
    • Adaptive interfaces for immersive media (VR/AR/MR)
    • Sensor-based and IoT-enhanced multimedia personalization
    • Real-time context adaptation in smart environments
  • Privacy and security in personalized media services
    • Ethical AI and bias mitigation in personalized media
    • Secure and privacy-preserving user profiling
    • Trustworthy AI for multimedia adaptation

This Special Issue welcomes original research papers, review articles, and application-oriented contributions that push the boundaries of semantic multimedia adaptation and personalized content delivery. We encourage interdisciplinary submissions that bridge the gap between multimedia computing, artificial intelligence, human–computer interaction, and cognitive sciences.

You may choose our Joint Special Issue in Digital.

Dr. Phivos Mylonas
Dr. Christos Troussas
Dr. Akrivi Krouska
Dr. Manolis Wallace
Prof. Dr. Cleo Sgouropoulou
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 250 words) can be sent to the Editorial Office for assessment.

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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • multimedia
  • user profiling
  • context-aware

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Related Special Issue

Published Papers (9 papers)

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Research

23 pages, 915 KB  
Article
Learning Scientific Document Representations via Triple-Source Automatic Supervision Without Annotations or Citations
by Mussa Turdalyuly, Ainur Tursynkhan, Aigerim Yerimbetova, Tolganay Turdalykyzy, Bakzhan Sakenov, Nurzhan Mukazhanov and Nazerke Baisholan
Computers 2026, 15(5), 268; https://doi.org/10.3390/computers15050268 - 23 Apr 2026
Viewed by 219
Abstract
Learning meaningful representations of scientific documents is essential for information retrieval, knowledge discovery, and recommendation systems. Traditional methods such as TF-IDF rely on lexical matching and fail to capture deeper semantic relationships, while transformer-based approaches typically depend on limited supervision signals. In this [...] Read more.
Learning meaningful representations of scientific documents is essential for information retrieval, knowledge discovery, and recommendation systems. Traditional methods such as TF-IDF rely on lexical matching and fail to capture deeper semantic relationships, while transformer-based approaches typically depend on limited supervision signals. In this work, we propose a Triple-Source automatic supervision framework for learning document embeddings from scientific corpora. The model integrates three types of supervision–title–abstract pairs, same-category document pairs, and document-level semantic relationships—within a unified contrastive learning framework based on a multilingual XLM-RoBERTa encoder. Unlike prior approaches that rely on citation graphs or manual annotations, our method enables citation-free and annotation-free representation learning using only lightweight metadata. Experiments on a publicly available arXiv dataset consisting of 98,649 documents demonstrate improved semantic retrieval performance, achieving Recall@1 = 0.6181 for same-category retrieval and outperforming both TF-IDF and single-source transformer baselines. The learned embeddings also exhibit improved clustering of scientific domains, indicating more structured semantic representations. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
23 pages, 1954 KB  
Article
Model-Contingent Polarity Bias in Large Language Model Annotation: Implications for Semantic Multimedia Personalization
by Constantinos Djouvas, Christiana Andreou, Maria C. Voutsa and Nicolas Tsapatsoulis
Computers 2026, 15(5), 262; https://doi.org/10.3390/computers15050262 - 22 Apr 2026
Viewed by 196
Abstract
Large Language Models (LLMs) are increasingly deployed as automated annotators in semantic multimedia systems, yet their reliability varies significantly across architectures. This study extends prior cross-model evaluations by benchmarking ChatGPT-5, Qwen-3, and Gemini-3-flash against human expert annotations using the HRAST hotel review dataset. [...] Read more.
Large Language Models (LLMs) are increasingly deployed as automated annotators in semantic multimedia systems, yet their reliability varies significantly across architectures. This study extends prior cross-model evaluations by benchmarking ChatGPT-5, Qwen-3, and Gemini-3-flash against human expert annotations using the HRAST hotel review dataset. We adopt a bias-by-design framework to analyze systematic divergences in sentiment, topic, and aspect labeling across real and synthetic data, while investigating the moderating effects of annotation mode. Findings reveal model-contingent polarity bias: ChatGPT-5 exhibits a pronounced neutrality bias, while Qwen-3 and Gemini-3-flash align more closely with human polarization. Agreement is substantial for concrete topics but diverges on abstract evaluative dimensions. Synthetic data consistently inflates reliability metrics while masking ambiguity. These findings highlight that annotation bias is structurally embedded in model design choices and operational conditions. Cross-architectural triangulation and mode-aware deployment strategies are recommended for robust semantic multimedia system development. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
22 pages, 1051 KB  
Article
An Ontology-Driven Framework for Personalised Context-Aware Running Event Recommendations
by Adisak Intana, Kuljaree Tantayakul, Wasupon Tanthavanich and Wachiravit Chumchuay
Computers 2026, 15(3), 195; https://doi.org/10.3390/computers15030195 - 19 Mar 2026
Viewed by 459
Abstract
Sport tourism has experienced significant growth within the tourism industry, driven by the increasing demand of special interest tourists to watch or participate in sports events with local sightseeing. However, the massive volume of available information related to sport events may cause challenges [...] Read more.
Sport tourism has experienced significant growth within the tourism industry, driven by the increasing demand of special interest tourists to watch or participate in sports events with local sightseeing. However, the massive volume of available information related to sport events may cause challenges to existing recommendation systems, which struggle to provide tailored suggestions for these niche tourists. Therefore, this paper proposes a novel, context-aware recommender framework that utilises the ontology-driven approach with unsupervised machine learning techniques to deliver personalised event matches for running tourists. Using an ontology-driven approach, the framework establishes a knowledge base of user profiles and running events. Furthermore, K-modes clustering was also applied to categorise participants based on their event participation characteristics, while the Apriori algorithm was used to uncover hidden relationships influencing event selection. To ensure the statistical integrity of the discovered association rule, permutation testing was implemented to mitigate bias inherent in small sample sizes. By integrating refined association rules with Jena rules, the resulting prototype offers adaptive, personalised, and contextually relevant running event recommendations that evolve with shifting user preferences and trends. The effectiveness of the prototype is confirmed through rigorous validation and evaluation across various sport tourism scenarios. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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29 pages, 477 KB  
Article
Sem4EDA: A Knowledge-Graph and Rule-Based Framework for Automated Fault Detection and Energy Optimization in EDA-IoT Systems
by Antonios Pliatsios and Michael Dossis
Computers 2026, 15(2), 103; https://doi.org/10.3390/computers15020103 - 2 Feb 2026
Viewed by 778
Abstract
This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges [...] Read more.
This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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24 pages, 469 KB  
Article
Cross-Lingual Adaptation for Multilingual Table Question Answering and Comparative Evaluation with Large Language Models
by Sanghyun Cho, Minho Kim, Hye-Lynn Kim, Jung-Hun Lee, Hyuk-Chul Kwon and Soo-Jong Lim
Computers 2026, 15(2), 92; https://doi.org/10.3390/computers15020092 - 1 Feb 2026
Viewed by 661
Abstract
Table question answering has been studied using datasets drawn from a variety of tabular sources and task formats. However, most publicly available resources have been created in high-resource languages such as English. For low-resource languages, researchers are often required to construct new datasets [...] Read more.
Table question answering has been studied using datasets drawn from a variety of tabular sources and task formats. However, most publicly available resources have been created in high-resource languages such as English. For low-resource languages, researchers are often required to construct new datasets or translate existing ones, which incurs substantial time, effort, and financial cost. In contrast to natural language text, table data consists of structured entries whose interpretation is less affected by language-specific syntax or word order. In this work, we present a cost-effective strategy for multilingual table QA that relies on selectively translating only the questions of existing datasets. Leveraging the language-agnostic structure of tables, our approach maintains the original table content while translating queries into multiple target languages. To address possible performance drops caused by using table data in the source language rather than the target language, we apply cross-lingual adaptation techniques using contrastive learning and adversarial training. In addition, to strengthen reasoning ability while avoiding degradation in languages not seen during pre-training, we perform supplementary pre-training of a RoBERTa-based multilingual encoder with SQL-derived table data. Finally, we extend our investigation beyond encoder-based architectures and evaluate decoder-only large language models under the same multilingual table QA setting. The experiments show that LLaMA-3 models exhibit strong cross-lingual generalization even without using translated table context and often achieve competitive performance using only Korean table data. Moreover, the performance gap among training configurations such as translated queries or translated datasets is notably smaller compared to encoder-based models, highlighting the inherent multilingual robustness of modern LLMs. We further evaluate LLaMA-3 models on domain-specific table datasets and observe that domain knowledge acquired from Korean tables transfers effectively across languages even without multilingual supervision, underscoring the potential of LLMs for specialized multilingual table reasoning. These findings demonstrate that LLMs can serve as an effective alternative for multilingual table QA, particularly in low-resource or partially translated environments. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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27 pages, 1434 KB  
Article
An ML-Based Approach to Leveraging Social Media for Disaster Type Classification and Analysis Across World Regions
by Mohammad Robel Miah, Lija Akter, Ahmed Abdelmoamen Ahmed, Louis Ngamassi and Thiagarajan Ramakrishnan
Computers 2026, 15(1), 16; https://doi.org/10.3390/computers15010016 - 1 Jan 2026
Viewed by 702
Abstract
Over the past decade, the frequency and impact of both natural and human-induced disasters have increased significantly, highlighting the urgent need for effective and timely relief operations. Disaster response requires efficient allocation of resources to the right locations and disaster types in a [...] Read more.
Over the past decade, the frequency and impact of both natural and human-induced disasters have increased significantly, highlighting the urgent need for effective and timely relief operations. Disaster response requires efficient allocation of resources to the right locations and disaster types in a cost- and time-effective manner. However, during such events, large volumes of unverified and rapidly spreading information—especially on social media—often complicate situational awareness and decision-making. Consequently, extracting actionable insights and accurately classifying disaster-related information from social media platforms has become a critical research challenge. Machine Learning (ML) approaches have shown strong potential for categorizing disaster-related tweets, yet substantial variations in model accuracy persist across disaster types and regional contexts, suggesting that universal models may overlook linguistic and cultural nuances. This paper investigates the categorization and sub-categorization of natural disaster tweets using a labeled dataset of over 32,000 samples. Logistic Regression and Random Forest classifiers were trained and evaluated after comprehensive preprocessing to predict disaster categories and sub-categories. Furthermore, a country-specific prediction framework was implemented to assess how regional and cultural variations influence model performance. The results demonstrate strong overall classification accuracy, while revealing marked differences across countries, emphasizing the importance of context-aware, culturally adaptive ML approaches for reliable disaster information management. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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22 pages, 964 KB  
Article
Multi-Modal Emotion Detection and Tracking System Using AI Techniques
by Werner Mostert, Anish Kurien and Karim Djouani
Computers 2025, 14(10), 441; https://doi.org/10.3390/computers14100441 - 16 Oct 2025
Viewed by 3347
Abstract
Emotion detection significantly impacts healthcare by enabling personalized patient care and improving treatment outcomes. Single-modality emotion recognition often lacks reliability due to the complexity and subjectivity of human emotions. This study proposes a multi-modal emotion detection platform integrating visual, audio, and heart rate [...] Read more.
Emotion detection significantly impacts healthcare by enabling personalized patient care and improving treatment outcomes. Single-modality emotion recognition often lacks reliability due to the complexity and subjectivity of human emotions. This study proposes a multi-modal emotion detection platform integrating visual, audio, and heart rate data using AI techniques, including convolutional neural networks and support vector machines. The system outperformed single-modality approaches, demonstrating enhanced accuracy and robustness. This improvement underscores the value of multi-modal AI in emotion detection, offering potential benefits across healthcare, education, and human–computer interaction. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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40 pages, 3285 KB  
Article
SemaTopic: A Framework for Semantic-Adaptive Probabilistic Topic Modeling
by Amani Drissi, Salma Sassi, Richard Chbeir, Anis Tissaoui and Abderrazek Jemai
Computers 2025, 14(9), 400; https://doi.org/10.3390/computers14090400 - 19 Sep 2025
Cited by 1 | Viewed by 1663
Abstract
Topic modeling is a crucial technique for Natural Language Processing (NLP) which helps to automatically uncover coherent topics from large-scale text corpora. Yet, classic methods tend to suffer from poor semantic depth and topic coherence. In this regard, we present here a new [...] Read more.
Topic modeling is a crucial technique for Natural Language Processing (NLP) which helps to automatically uncover coherent topics from large-scale text corpora. Yet, classic methods tend to suffer from poor semantic depth and topic coherence. In this regard, we present here a new approach “SemaTopic” to improve the quality and interpretability of discovered topics. By exploiting semantic understanding and stronger clustering dynamics, our approach results in a more continuous, finer and more stable representation of the topics. Experimental results demonstrate that SemaTopic achieves a relative gain of +6.2% in semantic coherence compared to BERTopic on the 20 Newsgroups dataset (Cv=0.5315 vs. 0.5004), while maintaining stable performance across heterogeneous and multilingual corpora. These findings highlight “SemaTopic” as a scalable and reliable solution for practical text mining and knowledge discovery. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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30 pages, 10155 KB  
Article
Interoperable Semantic Systems in Public Administration: AI-Driven Data Mining from Law-Enforcement Reports
by Alexandros Z. Spyropoulos and Vassilis Tsiantos
Computers 2025, 14(9), 376; https://doi.org/10.3390/computers14090376 - 8 Sep 2025
Cited by 2 | Viewed by 3076
Abstract
The digitisation of law-enforcement archives is examined with the aim of moving from static analogue records to interoperable semantic information systems. A step-by-step framework for optimal digitisation is proposed, grounded in archival best practice and enriched with artificial-intelligence and semantic-web technologies. Emphasis is [...] Read more.
The digitisation of law-enforcement archives is examined with the aim of moving from static analogue records to interoperable semantic information systems. A step-by-step framework for optimal digitisation is proposed, grounded in archival best practice and enriched with artificial-intelligence and semantic-web technologies. Emphasis is placed on semantic data representation, which renders information actionable, searchable, interlinked, and automatically processed. As a proof of concept, a large language model—OpenAI ChatGPT, version o3—was applied to a corpus of narrative police reports, extracting and classifying key entities (metadata, persons, addresses, vehicles, incidents, fingerprints, and inter-entity relationships). The output was converted to Resource Description Framework triples and ingested into a triplestore, demonstrating how unstructured text can be transformed into machine-readable, interoperable data with minimal human intervention. The approach’s challenges—technical complexity, data quality assurance, information-security requirements, and staff training—are analysed alongside the opportunities it affords, such as accelerated access to records, cross-agency interoperability, and advanced analytics for investigative and strategic decision-making. Combining systematic digitisation, AI-driven data extraction, and rigorous semantic modelling ultimately delivers a fully interoperable information environment for law-enforcement agencies, enhancing efficiency, transparency, and evidentiary integrity. Full article
(This article belongs to the Special Issue Advances in Semantic Multimedia and Personalized Digital Content)
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