Advances in Recommender Systems and Intelligent Agents

Dear Colleagues,

Recommender systems have become an integral part of our daily lives, influencing our choices in various domains such as e-commerce, entertainment, and content consumption. Advancements in machine learning, data mining, and artificial intelligence have significantly enhanced the effectiveness and efficiency of these systems. This Special Issue aims to explore recent breakthroughs in recommender systems, including matrix factorization techniques, deep learning approaches, group recommendations, explainable recommender systems, fair recommendations, and context-aware recommendations.

Intelligent agents, on the other hand, have evolved to provide adaptive and personalized user experiences by leveraging advanced algorithms and decision-making processes. These agents have the potential to assist individuals in making informed decisions, optimizing resource allocation, and enhancing overall user satisfaction. This Special Issue invites innovative research on intelligent agents, including multi-agent systems, reinforcement learning, and cognitive architectures, with a strong emphasis on mathematical models and computational approaches.

This Special Issue aims to explore the latest breakthroughs in the fields of Recommender Systems and Intelligent Agents, bringing together cutting-edge research and advancements in the field of recommender systems and intelligent agents, with a strong mathematical foundation. We welcome original research articles, reviews, and case studies that contribute to the theoretical foundations and practical applications of recommender systems and intelligent agents. Topics of interest include, but are not limited to:

• Novel algorithms and methodologies for recommender systems;
• Hybrid approaches combining multiple recommendation techniques;
• Scalability and efficiency enhancements for large-scale systems;
• User modeling and personalized recommendations;
• Trust, privacy, and fairness in recommender systems;
• Explainability and transparency in recommendation techniques;
• Multi-agent coordination and cooperation strategies;
• Social recommender systems;
• New datasets and evaluation methodologies for recommender systems.

We encourage interdisciplinary contributions that bridge the gap between mathematics, computer science, and behavioral sciences, fostering collaborations and innovation in the field. Researchers and practitioners from academia, industry, and other relevant domains are invited to submit their work for consideration.

Dr. Silvia N. Schiaffino
Dr. Marcelo Gabriel Armentano
Dr. Ariel Monteserin
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. 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.

• recommender systems
• machine learning
• intelligent agents
• personalization
• user modeling
• intelligent systems