Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.3
Journals
Mathematics
Special Issues
Advances in Recommender Systems and Intelligent Agents
share announcement

Advances in Recommender Systems and Intelligent Agents

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1831

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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
Viewed by 693
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)
►▼ Show Figures

Figure 1

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 599
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop