AI-Supported Decision Making and Recommender Systems

Dear Colleagues,

In the era of digital transformation, Artificial Intelligence (AI) has emerged as a key technology, reshaping industries and transforming how we live and work. AI-driven systems not only enhance efficiency and accuracy, but also demonstrate the flexibility to adapt to user preferences, business goals, and dynamic environments. As data grow more complex and the demand for real-time, accurate decision making tools increases, the role of AI in decision making and recommender systems has become indispensable. Through techniques such as computational intelligence, machine learning, deep learning, and natural language processing, AI technologies can automatically identify patterns, handle uncertainty, optimize decision making, and provide personalized, adaptive recommendations tailored to both individual and organizational needs. The integration of AI into these systems has not only improved performance and scalability, but also introduced new methods to address complex, multidimensional challenges in domains like healthcare, finance, and e-commerce.

We are pleased to invite submissions to this Special Issue, which aims to explore the transformative impact of AI on decision making processes and recommendation systems across various domains. This Special Issue seeks to showcase cutting-edge research, methodologies, and applications that leverage AI to improve the efficiency, accuracy, and effectiveness of decision making and recommendations.

We welcome original research articles and reviews. Topics of interest include, but are not limited to:

• AI-enabled multi-criteria decision analysis;
• AI-enabled group decision making;
• Computational intelligence-based decision making;
• Decision making with generative AI;
• Preference learning in decision making;
• Machine learning models for decision support systems;
• AI-driven recommendation algorithms, including multimodal, cross-domain, knowledge graph-based and sequential recommendation algorithms;
• Fairness, diversity, and trustworthiness in recommender systems;
• Hybrid recommender systems integrating AI;
• Explainable AI in decision making and recommendation systems;
• Applications of AI-supported decision making and recommender systems in various domains (e.g., healthcare, finance, e-commerce, education).

Dr. Zhen Zhang
Prof. Dr. Jian Wu
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. Applied Sciences 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.

• artificial intelligence
• decision making
• recommender systems