Recommender Systems: Advanced Topics and Applications

Dear Colleagues,

Recommender systems have emerged as essential tools for navigating the vast landscape of digital information, offering personalized suggestions across domains such as e-commerce, entertainment, healthcare, and education. While traditional approaches like collaborative and content-based filtering have laid the foundation, modern advancements have pushed the boundaries of what these systems can achieve. Techniques leveraging deep learning, reinforcement learning, and federated learning now enable recommender systems to deliver context-sensitive, explainable, and multi-stakeholder solutions that address diverse and complex user needs. Despite these advancements, challenges remain in areas such as fairness, transparency, sustainability, robustness, and the ethical use of data, particularly when balancing personalization with those areas. An exciting development lies in explainable recommendations, which aim to build user trust by clarifying why specific items are suggested. Moreover, advanced applications such as conversational agents and real-time recommendation pipelines are bridging the gap between user preferences and immediate decision-making needs.

This Special Issue on "Recommender Systems: Advanced Topics and Applications" seeks to explore the latest developments and challenges in the field. By delving into advanced techniques and novel applications, we aim to foster a comprehensive understanding of the evolving landscape of recommender systems. Submissions are encouraged on topics such as the following:

• Explainable and interpretable recommender systems;
• Deep neural models for recommender systems;
• Reinforcement learning for recommender systems;
• Applications of large language models in recommender systems;
• Federated learning and privacy-preserving approaches;
• Multimodal recommender systems;
• Multi-stakeholder recommendation approaches;
• Multi-objective recommender systems;
• Long-term optimization strategies for recommender systems;
• Cross-domain recommendation techniques;
• Ethical and fairness challenges in recommender systems;
• Scalable solutions for large-scale recommendation problems;
• Recommender system evaluation and metrics.

Dr. Masoud Mansoury
Guest Editor

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.

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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.

• recommender systems
• collaborative filtering
• content-based recommendation
• fair ranking
• transparency
• trustworthy recommendation systems
• user modelling
• cold start
• diversity and novelty