Recommender Systems and Data Mining

Dear Colleagues,

With an ever-widening range of areas where recommender systems are successfully proving their applicability, having the opportunity to gather the latest advances in recommender systems and data mining will give us the opportunity to have a complete platform to present and discuss the latest developments in this field. The following points can be highlighted:

1. Focus on cutting-edge research: This Special Issue will showcase the latest and most innovative research in the field, making it an ideal platform for authors to share their work and ideas.
2. High visibility: This Special Issue will be widely disseminated, providing authors with a high-visibility platform to reach a broad and influential audience.
3. Interdisciplinary nature: Recommender systems and data mining are interdisciplinary fields that bring together researchers from a variety of backgrounds. This Special Issue will provide an opportunity for these researchers to collaborate and share their knowledge.
4. Relevance: Recommender systems and data mining play a critical role in many real-world applications and are of great interest to researchers, practitioners, and policymakers. The Special Issue will provide valuable insights into this rapidly evolving field.
5. Expert review: All submissions will undergo a rigorous review process, ensuring that only the highest-quality research is accepted for publication.

By submitting to this Special Issue, authors will have the opportunity to make a significant contribution to the advancement of the field, while also raising their own profile and that of their research.

This Special Issue aims to cover the recent emerging trends and applications for Recommender Systems and Data Mining. Topics of interest include, but are not limited to:

• Context-aware Recommender Systems;
• Affective Recommender Systems;
• Trustworthy Recommender Systems;
• Intelligent Recommender Systems in User-centred Scenarios (e.g., medicine, education, transportation, etc.);
• Human Decision Making in Recommender Systems;
• Implicit versus Explicit Feedback in Recommender Systems;
• Content-based Recommender Systems;
• Context-aware Recommender Systems;
• Personalization in Recommender Systems;
• Deep learning/reinforcement learning/federated learning in Recommender Systems;
• Meta-learning in Recommender Systems;
• Hybrid Recommender Systems;
• Recommender System Evaluation and Metrics;
• Scalability and Performance of Recommender Systems;
• Explanation and Interpretability of Recommender Systems;
• Ethical and Privacy Issues in Recommender Systems.

Prof. Dr. Jesús G. Boticario
Prof. Dr. Jose M. Alcaraz Calero
Prof. Dr. David Martín Gómez
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. 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.