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 Challenges and New Opportunities for Next-Generation Recommender Systems
截稿日期: 2025-04-15

Special Issue Link: https://www.mdpi.com/journal/electronics/special_issues/Next_Generation_Recommender_Systems

Recommender systems rely on users' explicit or/and latent interests to offer personalized suggestions of items automatically, aiming to help users cope with information overload. Despite its indispensable role in the areas of e-commerce, targeted advertising, intelligent medical assistants and online media, recommender systems are still facing many challenges which require a deeper analysis of both the user, the content, and their relationships, such as the context-awareness, the (sequential) user behavior modeling, the explainability, diversity, and fairness of recommender systems, multi-stakeholder recommendation, data sparsity issues as well as misinformation detection. Fortunately, with the advancement of deep learning, multi-modal data have become better accessible for AI technology. The great success of language modelling techniques also inspires recommendation models to alleviate the aforementioned challenges through knowledge transmission and distillation from the pre-trained models.

This special issue solicits the latest and significant contributions on developing and applying efficient user modeling and advanced machine learning techniques for building next-generation recommender systems and particularly on data and model-driven intelligent recommender systems.

Topics of interest include but are not limited to, the following:

* Cross-domain/source recommendation
* Context-aware/group/multi-criteria/multi-objective recommender systems
* Multi-modal recommendation
* Explainanality/transparency in recommender systems
* Fairness/bias in recommender systems
* Efficient and scalable recommendation techniques
* Pre-trained recommendation model
* Self-supervised learning for recommendation
* Data sparsity, cold start and long-tail issues in recommender systems
* Knowledge transfer and distillation in recommender systems
* Surveys, reviews and prospects on next-generation recommender systems

Manuscript submission deadline: 15th April 2025