Artificial Intelligence and Algorithms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 962

Special Issue Editor


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Department of Information Management, Chinese Culture University Taiwan, Taipei, Taiwan
Interests: artificial intelligence; eLearning; interconnection networks; graph theory; algorithms

Special Issue Information

Dear Colleagues,

The Special Issue, “Artificial Intelligence and Algorithms”, aims to delve into the mathematical foundations and innovative advancements in the rapidly evolving fields of artificial intelligence (AI) and algorithm development. As AI technologies continue to revolutionize various domains, the role of sophisticated mathematical models and algorithms becomes increasingly critical in driving these innovations. This collection seeks to explore the theoretical and foundational aspects of algorithms that underlie AI systems. This includes work in algorithmic complexity, computational models, optimization techniques, and new paradigms in AI.

This Special Issue will highlight key areas where mathematics intersects with AI, including but not limited to optimization techniques, statistical methods, machine learning algorithms, and computational complexity. We seek to explore how mathematical theories and frameworks underpin the development of intelligent systems that can learn, adapt, and make decisions. Topics of interest include the design and analysis of algorithms for deep learning, reinforcement learning, natural language processing, and data mining, as well as the application of advanced mathematical tools such as linear algebra, probability theory, and differential equations in solving complex AI problems.

By bringing together contributions from mathematicians, computer scientists, and AI researchers, this Special Issue aims to provide a comprehensive overview of the state-of-the-art mathematical techniques driving AI. We invite theoretical and applied research papers that present novel mathematical models, innovative algorithms, and their practical applications in AI. This collection will serve as a valuable resource for researchers and practitioners looking to deepen their understanding of the mathematical principles that form the backbone of artificial intelligence and its algorithms.

Prof. Dr. Fuhsing Wang
Guest Editor

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Keywords

  • machine learning
  • deep learning
  • neural networks
  • natural language processing
  • computer vision
  • adaptive learning systems
  • big data
  • algorithm optimization
  • cognitive computing

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Published Papers (1 paper)

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Research

16 pages, 3451 KiB  
Article
Session-Based Recommendation Method Using Popularity-Stratified Preference Modeling
by Yayelin Mo and Haowen Wang
Mathematics 2025, 13(6), 960; https://doi.org/10.3390/math13060960 - 14 Mar 2025
Viewed by 440
Abstract
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity scoring calculations to [...] Read more.
Large-scale offline evaluations of user–project interactions in recommendation systems are often biased due to inherent feedback loops. To address this, many studies have employed propensity scoring. In this work, we extend these methods to session-based recommendation tasks by refining propensity scoring calculations to reflect dataset-specific characteristics. We evaluate our approach using neural models, specifically GRU4REC, and K-Nearest Neighbors (KNN)-based models on music and e-commerce datasets. GRU4REC is selected for its proven sequential model and computational efficiency, serving as a robust baseline against which we compare traditional methods. Our analysis of trend distributions reveals significant variations across datasets, and based on these insights, we propose a hierarchical approach that enhances model performance. Experimental results demonstrate substantial improvements over baseline models, providing a clear pathway for mitigating biases in session-based recommendation systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Algorithms)
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