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Mathematics
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Advances in Recommender Systems and Intelligent Agents
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Advances in Recommender Systems and Intelligent Agents

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 4367

Special Issue Editors

Dr. Silvia N. Schiaffino

E-Mail Website
Guest Editor
ISISTAN Research Institute, National University of the Center of the Buenos Aires Province, National Scientific and Technical Research Council, CONICET, Rosario S2000EZP, Santa Fe, Argentina
Interests: recommender systems; user profiling; intelligent agents; personalization
Dr. Marcelo Gabriel Armentano

E-Mail Website
Guest Editor
ISISTAN Research Institute, National University of the Center of the Buenos Aires Province, National Scientific and Technical Research Council, CONICET, Rosario S2000EZP, Santa Fe, Argentina
Interests: recommender systems; user modeling; intelligent systems; personalization
Dr. Ariel Monteserin

E-Mail Website
Guest Editor
ISISTAN Research Institute, National University of the Center of the Buenos Aires Province, National Scientific and Technical Research Council, CONICET, Rosario S2000EZP, Santa Fe, Argentina
Interests: negotiation among intelligent agents and multiagent systems; crowdsensing in smart cities

Special Issue Information

Dear Colleagues,

Recommender systems have become an integral part of our daily lives, influencing our choices in various domains such as e-commerce, entertainment, and content consumption. Advancements in machine learning, data mining, and artificial intelligence have significantly enhanced the effectiveness and efficiency of these systems. This Special Issue aims to explore recent breakthroughs in recommender systems, including matrix factorization techniques, deep learning approaches, group recommendations, explainable recommender systems, fair recommendations, and context-aware recommendations.

Intelligent agents, on the other hand, have evolved to provide adaptive and personalized user experiences by leveraging advanced algorithms and decision-making processes. These agents have the potential to assist individuals in making informed decisions, optimizing resource allocation, and enhancing overall user satisfaction. This Special Issue invites innovative research on intelligent agents, including multi-agent systems, reinforcement learning, and cognitive architectures, with a strong emphasis on mathematical models and computational approaches.

This Special Issue aims to explore the latest breakthroughs in the fields of Recommender Systems and Intelligent Agents, bringing together cutting-edge research and advancements in the field of recommender systems and intelligent agents, with a strong mathematical foundation. We welcome original research articles, reviews, and case studies that contribute to the theoretical foundations and practical applications of recommender systems and intelligent agents. Topics of interest include, but are not limited to:

  • Novel algorithms and methodologies for recommender systems;
  • Hybrid approaches combining multiple recommendation techniques;
  • Scalability and efficiency enhancements for large-scale systems;
  • User modeling and personalized recommendations;
  • Trust, privacy, and fairness in recommender systems;
  • Explainability and transparency in recommendation techniques;
  • Multi-agent coordination and cooperation strategies;
  • Social recommender systems;
  • New datasets and evaluation methodologies for recommender systems.

We encourage interdisciplinary contributions that bridge the gap between mathematics, computer science, and behavioral sciences, fostering collaborations and innovation in the field. Researchers and practitioners from academia, industry, and other relevant domains are invited to submit their work for consideration.

Dr. Silvia N. Schiaffino
Dr. Marcelo Gabriel Armentano
Dr. Ariel Monteserin
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. Mathematics 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 2600 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.

Keywords

  • recommender systems
  • machine learning
  • intelligent agents
  • personalization
  • user modeling
  • intelligent systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

16 pages, 2628 KiB  
Article
Improving Recommender Systems for Fake News Detection in Social Networks with Knowledge Graphs and Graph Attention Networks
by Aleksei Golovin, Nataly Zhukova, Radhakrishnan Delhibabu and Alexey Subbotin
Mathematics 2025, 13(6), 1011; https://doi.org/10.3390/math13061011 - 20 Mar 2025
Viewed by 374
Abstract
This paper addresses the pervasive problem of fake news propagation in social networks. Traditional text-based detection models often suffer from performance degradation over time due to their reliance on evolving textual features. To overcome this limitation, we propose a novel recommender system that [...] Read more.
This paper addresses the pervasive problem of fake news propagation in social networks. Traditional text-based detection models often suffer from performance degradation over time due to their reliance on evolving textual features. To overcome this limitation, we propose a novel recommender system that leverages the power of knowledge graphs and graph attention networks (GATs). This approach captures both the semantic relationships within the news content and the underlying social network structure, enabling more accurate and robust fake news detection. The GAT model, by assigning different weights to neighboring nodes, effectively captures the importance of various users in disseminating information. We conducted a comprehensive evaluation of our system using the FakeNewsNet dataset, comparing its performance against classical machine learning models and the DistilBERT language model. Our results demonstrate that the proposed graph-based system achieves state-of-the-art performance, with an F1-score of 95%, significantly outperforming other models. Moreover, it maintains its effectiveness over time, unlike text-based approaches that are susceptible to concept drift. This research underscores the potential of knowledge graphs and GATs in combating fake news and provides a robust framework for building more resilient and accurate detection systems. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Intelligent Agents)
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19 pages, 326 KiB  
Article
Multi-View Contrastive Fusion POI Recommendation Based on Hypergraph Neural Network
by Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Ya Liu, Junhao Wen and Zhengyi Yang
Mathematics 2025, 13(6), 998; https://doi.org/10.3390/math13060998 - 19 Mar 2025
Viewed by 259
Abstract
In the era of information overload, location-based social software has gained widespread popularity, and the demand for personalized POI (Point of Interest) recommendation services is growing rapidly. Recommending the next POI is crucial in recommendation systems, aiming to suggest appropriate next-visit locations based [...] Read more.
In the era of information overload, location-based social software has gained widespread popularity, and the demand for personalized POI (Point of Interest) recommendation services is growing rapidly. Recommending the next POI is crucial in recommendation systems, aiming to suggest appropriate next-visit locations based on users’ historical trajectories and check-in data. However, the existing research often neglects user preferences’ diversity and dynamic nature and the need for the deep modeling of key collaborative relationships across various dimensions. As a result, the recommendation performance is limited. To address these challenges, this paper introduces an innovative Multi-View Contrastive Fusion Hypergraph Learning Model (MVHGAT). The model first constructs three distinct hypergraphs, representing interaction, trajectory, and geographical location, capturing the complex relationships and high-order dependencies between users and POIs from different perspectives. Subsequently, a targeted hypergraph convolutional network is designed for aggregation and propagation, learning the latent factors within each view. Through multi-view weighted contrastive learning, the model uncovers key collaborative effects between views, enhancing both user and POI representations’ consistency and discriminative power. The experimental results demonstrate that MVHGAT significantly outperforms several state-of-the-art methods across three public datasets, effectively addressing issues such as data sparsity and oversmoothing. This model provides new insights and solutions for the next POI recommendation task. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Intelligent Agents)
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28 pages, 4455 KiB  
Article
Leveraging ChatGPT and Long Short-Term Memory in Recommender Algorithm for Self-Management of Cardiovascular Risk Factors
by Tatiana V. Afanasieva, Pavel V. Platov, Andrey V. Komolov and Andrey V. Kuzlyakin
Mathematics 2024, 12(16), 2582; https://doi.org/10.3390/math12162582 - 21 Aug 2024
Cited by 1 | Viewed by 1553
Abstract
One of the new trends in the development of recommendation algorithms is the dissemination of their capabilities to support the population in managing their health, in particular cardiovascular health. Cardiovascular diseases (CVDs) affect people in their prime years and remain the main cause [...] Read more.
One of the new trends in the development of recommendation algorithms is the dissemination of their capabilities to support the population in managing their health, in particular cardiovascular health. Cardiovascular diseases (CVDs) affect people in their prime years and remain the main cause of morbidity and mortality worldwide, and their clinical treatment is expensive and time consuming. At the same time, about 80% of them can be prevented, according to the World Federation of Cardiology. The aim of this study is to develop and investigate a knowledge-based recommender algorithm for the self-management of CVD risk factors in adults at home. The proposed algorithm is based on the original user profile, which includes a predictive assessment of the presence of CVD. To obtain a predictive score for CVD presence, AutoML and LSTM models were studied on the Kaggle dataset, and it was shown that the LSTM model, with an accuracy of 0.88, outperformed the AutoML model. The algorithm recommendations generated contain items of three types: targeted, informational, and explanatory. For the first time, large language models, namely ChatGPT-3.5, ChatGPT-4, and ChatGPT-4.o, were leveraged and studied in creating explanations of the recommendations. The experiments show the following: (1) In explaining recommendations, ChatGPT-3.5, ChatGPT-4, and ChatGPT-4.o demonstrate a high accuracy of 71% to 91% and coherence with modern official guidelines of 84% to 92%. (2) The safety properties of ChatGPT-generated explanations estimated by doctors received the highest score of almost 100%. (3) On average, the stability and correctness of the GPT-4.o responses were more acceptable than those of other models for creating explanations. (4) The degree of user satisfaction with the recommendations obtained using the proposed algorithm was 88%, and the rating of the usefulness of the recommendations was 92%. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Intelligent Agents)
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14 pages, 1109 KiB  
Article
EDiffuRec: An Enhanced Diffusion Model for Sequential Recommendation
by Hanbyul Lee and Junghyun Kim
Mathematics 2024, 12(12), 1795; https://doi.org/10.3390/math12121795 - 8 Jun 2024
Viewed by 1462
Abstract
Sequential recommender models should capture evolving user preferences over time, but there is a risk of obtaining biased results such as false positives and false negatives due to noisy interactions. Generative models effectively learn the underlying distribution and uncertainty of the given data [...] Read more.
Sequential recommender models should capture evolving user preferences over time, but there is a risk of obtaining biased results such as false positives and false negatives due to noisy interactions. Generative models effectively learn the underlying distribution and uncertainty of the given data to generate new data, and they exhibit robustness against noise. In particular, utilizing the Diffusion model, which generates data through a multi-step process of adding and removing noise, enables stable and effective recommendations. The Diffusion model typically leverages a Gaussian distribution with a mean fixed at zero, but there is potential for performance improvement in generative models by employing distributions with higher degrees of freedom. Therefore, we propose a Diffusion model-based sequential recommender model that uses a new noise distribution. The proposed model improves performance through a Weibull distribution with two parameters determining shape and scale, a modified Transformer architecture based on Macaron Net, normalized loss, and a learning rate warmup strategy. Experimental results on four types of real-world e-commerce data show that the proposed model achieved performance gains ranging from a minimum of 2.53% to a maximum of 13.52% across HR@K and NDCG@K metrics compared to the existing Diffusion model-based sequential recommender model. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Intelligent Agents)
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