Aims

Electronics (ISSN 2079-9292) is an international, peer-reviewed, open access journal on the science of electronics and its applications. It publishes reviews, research articles, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the maximum length of the papers. Full experimental and/or methodical details must be provided.

Subject Areas:

The scope of Electronics includes:

    Microelectronics
    Optoelectronics
    Industrial Electronics
    Power Electronics
    Bioelectronics
    Microwave and Wireless Communications
    Computer Science & Engineering
    Networks
    Systems & Control Engineering
    Circuit and Signal Processing
    Semiconductor Devices
    Artificial Intelligence
    Electrical and Autonomous Vehicles
    Quantum Electronics
    Flexible Electronics
    Artificial Intelligence Circuits and Systems (AICAS)
    Electronic Multimedia
    Electronic Materials

Special Issue on Large Language Models for Recommender Systems
截稿日期: 2026-04-30

Dear Colleagues,

Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence, offering unprecedented capabilities in natural language understanding, reasoning, and generative tasks. In parallel, recommender systems remain a cornerstone of digital platforms, guiding user decisions in domains such as e-commerce, media, education, and healthcare. The intersection of these two areas opens new opportunities for building highly personalized, context-aware, and conversational recommendation services. Unlike traditional recommenders that primarily rely on structured interaction data, LLM-powered recommenders can leverage unstructured content, dialogue history, and user intent expressed in natural language. This shift not only enhances user experience but also raises new research challenges in scalability, fairness, explainability, and evaluation.

This Special Issue aims at providing a dedicated venue for advancing research at the convergence of LLMs and recommender systems. We seek to explore how LLMs can enrich recommender pipelines—ranging from candidate generation and ranking to explanation and interactive recommendation—and how recommender system requirements can in turn shape the development of more efficient and trustworthy LLMs. This Special Issue aligns with the journal’s mission to showcase cutting-edge research in artificial intelligence, data-driven systems, and human-centered computing. Contributions will highlight both theoretical advances and practical deployments, fostering dialogue between the recommender systems and natural language processing communities.

We invite contributions on topics including, but not limited to, the following:

1. Architectures and frameworks for integrating LLMs with traditional recommender pipelines.
2. Prompt engineering, fine-tuning, and alignment strategies for recommendation tasks.
3. LLMs for conversational and dialogue-based recommender systems.
4. Multimodal recommendation leveraging LLMs (e.g., text, image, video, and audio).
5. Scalability, efficiency, and resource optimization in LLM-powered recommendation.
6. Fairness, bias mitigation, explainability, and transparency.
7. Human–AI collaboration and user experience design.
8. Benchmarks, evaluation methodologies, and reproducibility in LLM-based recommendation research.
9. Real-world applications and case studies across industries such as retail, media, healthcare, and education.

In this Special Issue, we welcome both original research articles and comprehensive review papers that push the boundaries of knowledge in this timely and impactful area.

We look forward to receiving your contributions.

Dr. Yong Zheng
Dr. Peng Liu
Guest Editors

Special Issue on Algorithmic Advances in Reinforcement Learning: Theory and Applications
截稿日期: 2026-04-30

Special Issue Information

Dear Colleagues,

Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and optimization in complex, dynamic, and uncertain environments. In recent years, we have witnessed remarkable algorithmic advances that bridge theoretical foundations with diverse real-world applications such as robotics, healthcare, finance, and autonomous systems. Despite these developments, challenges remain around improving sample efficiency, ensuring stability, and establishing rigorous performance guarantees.

This Special Issue, ‘Algorithmic Advances in Reinforcement Learning: Theory and Applications’, aims to bring together high-quality contributions that advance the state of the art in both theoretical and applied aspects of RL. We welcome submissions that address novel algorithms, convergence analysis, exploration–exploitation strategies, and hierarchical and multi-agent settings, as well as interdisciplinary applications. Both methodological papers and application-driven studies that include theoretical insights are encouraged.

We look forward to your valuable contributions, which will help to foster a deeper understanding of RL and its transformative potential across domains.

Dr. Yinglong Dai
Dr. Ke Li
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. Mathematics 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 2600 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:
reinforcement learning
algorithmic advances
theory and convergence
exploration and exploitation
hierarchical learning
multi-agent systems
real-world applications