New Trends in Recommender System: AI Algorithms, Mathematical Models and New Directions

Dear Colleagues,

Recommender systems and data mining are interdisciplinary fields intensively developed both in the area of new research methods and applications. A recommendation system is a search system in which the input query is a collection of user and context information, and the result is a ranked list of items. Recommendation (ranked) lists are generated based on the user’s preferences, the item’s characteristics, the user’s previous interactions with the item, and other additional information such as temporal and spatial data. Given a query, the task of the recommendation system is to find relevant items in the database and then rank the items based on specific goals.

The rapid development of artificial intelligence methods, computational intelligence methods, evolutionary methods, and data science has enabled their use in recommender systems to improve prediction accuracy and solve missing data problems.

This Special Issue reviews state-of-the-art artificial intelligence methods in recommender systems, including models, methods, and applications, and attempts to answer the question 'what are the trends in the use of recommender systems and research using machine learning algorithms’.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

- Machine learning algorithms in recommender systems;

- Deep learning technologies and multimodal data analysis;

- Trends in the use of recommender systems and research in the implementation of machine learning algorithms;

- Dimensionality reduction in recommender systems;

- Classifiers in recommender systems;

- Recommender systems for Internet of Things;

- Recommender systems with AI;

- Deep neural networks in recommender systems;

- CNN-based recommender systems;

- RNN-based recommender systems;

- Fuzzy techniques in recommender systems;

- Evolutionary algorithms in recommender systems;

- Computer vision in recommender systems;

- Privacy preserving and secure recommender systems;

- Blockchain and IoT-based recommender and cognitive systems.

We look forward to receiving your contributions.

Prof. Dr. Ryszard S. Choraś
Prof. Dr. Vaclav Snasel
Guest Editors

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• recommender systems
• artificial intelligence
• computational intelligence
• deep learning
• content-based filtering
• collaborative filtering
• fuzzy techniques