Smart Technologies in Recommender Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 January 2026 | Viewed by 49
Special Issue Editors
Interests: NLP in e-healthcare; data mining (R recommendation & summarization); big data processing
Special Issues, Collections and Topics in MDPI journals
Interests: recommender systems; algorithm design; topic modeling
Special Issues, Collections and Topics in MDPI journals
Interests: object tracking; hashing; large-scale image retrieval; simultaneous localization and mapping; camera calibration; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The exponential growth of digital content and the increasing sophistication of artificial intelligence (AI) and machine/deep learning (ML/DL) technologies have revolutionized the landscape of recommender systems. This Special Issue on “Smart Technologies in Recommender Systems” seeks to showcase pioneering research that harnesses intelligent technologies to create more accurate, personalized, and context-aware recommendation solutions across diverse domains.
We invite high-quality original research contributions exploring (but not limited to) the following topics:
- Advanced Machine/Deep Learning for Recommendations
- State-of-the-art neural architectures for collaborative filtering and content-based recommendations;
- Graph neural networks and knowledge graph embeddings for recommendation;
- Transformer-based models and attention mechanisms in recommender systems;
- Multi-modal learning approaches for cross-domain recommendations.
- Intelligent Personalization and User Modeling
- Real-time user preference learning and adaptive recommendation strategies;
- Context-aware recommendation incorporating temporal, spatial, and social factors;
- Explainable AI techniques for transparent and trustworthy recommendations;
- Privacy-preserving personalization methods.
- Smart Recommendation Applications
- Recommender systems for e-commerce, entertainment, and digital media;
- Healthcare recommendation systems for treatment plans and lifestyle choices;
- Educational recommender systems for personalized learning paths;
- IoT-enabled smart home and city recommendations.
- Human-Centered and Ethical Recommendation Design
- Addressing bias, fairness, and diversity in recommendation algorithms;
- User control and feedback mechanisms in recommender systems;
- Conversational and interactive recommendation interfaces;
- Responsible AI practices for recommendation deployment.
Dr. Gun-Woo Kim
Dr. Sang-Min Choi
Dr. Suwon Lee
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 recommender systems
- deep learning for recommendations
- personalized user modelling
- context-aware filtering
- graph neural networks
- explainable recommendations
- multi-modal learning
- conversational AI
- recommendation fairness
- privacy-preserving personalization
- IoT-enhanced recommendations
- human-in-the-loop systems
- ethical AI
- real-time adaptation
- cross-domain recommendations
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