Modern Recommender Systems 2026 : 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications

2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications
A special issue of Information (ISSN 2078-2489).

This special issue belongs to the section "Information Systems".

Special Issue Information
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Nowadays, recommender systems are indispensable in most personalized systems implementing information access and content delivery, supporting a great variety of user activities. Recommender systems alleviate the problem of information overload, identifying and promoting content that is deemed more suitable for each individual user. To this end, recommender systems collect and process information regarding user preferences, likings, and previous actions; the user’s current context (such as the user’s location or company, the time of day or week, etc.); the user’s neighborhood and activity in social networks (friends, posts, message exchanges, and so forth); the characteristics of items to be recommended, including semantic information; and so on. Both static and dynamic views of the collected data are considered, and the algorithms employed to process the available data range from collaborative filtering and statistical models to knowledge-based approaches and matrix factorization.

This Special Issue on “2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications” aims to promote new theoretical models, approaches, algorithms, and applications related to the area of recommender systems. Authors should submit papers describing significant, original, and unpublished work. Possible topics include, but are not limited to, the following:

* Models and algorithms to improve recommendation quality.
* Recommendation algorithms that exploit contextual information, social network information, and/or rich item descriptions.
* Techniques and methods for enhancing recommender system performance in the context of big data.
* Privacy-preserving techniques for recommender systems.
* Novel recommender system applications.
* Case studies of real-world implementations.
* Algorithm scalability, performance, and implementations.
* Cross-disciplinary approaches involving recommender systems.
* AI-based and explainable recommendations.

For more details, please visit the special issue page, https://www.mdpi.com/journal/information/special_issues/4UC6YN490Y

Special Issue Editors
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* Prof. Dr. Costas Vassilakis, Department of Informatics and Telecommunications, University of the Peloponnese, Greece, [email protected] https://users.uop.gr/users/costas
* Prof. Dionisis Margaris, Department of Digital Systems, University of the Peloponnese, Greece, [email protected], https://users.uop.gr/users/margaris