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Future Internet
Special Issues
Information Networks with Human-Centric LLMs
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Information Networks with Human-Centric LLMs

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 565

Special Issue Editors

Prof. Dr. Massimo Stella

E-Mail Website
Guest Editor
CogNosco Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
Interests: complex systems modeling; natural language processing; semantic networks; multilayer networks; complex networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giulio Rossetti

E-Mail Website
Guest Editor
Knowledge Discovery and Data Mining Laboratory, Information Science and Technologies Institute, Italian National Research Council, 56124 Pisa, PI, Italy
Interests: complex networks; dynamic networks; community discovery; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large Language Models (LLMs) have revolutionized our approach to information search and processing, and yet the investigation of LLM–LLM and LLM–human interactions in techno-social systems remains a scarcely explored research area.

Although they are powerful for a variety of purposes, LLMs remain black-box AI models: they can provide accurate classifications or predictions, but with little-to-no justification or interpretative power. Furthermore, although one could examine the behavior of one LLM in isolation, humans or other LLMs interacting together might give rise to unexpected mechanisms like coordination, empathy, or social bonds that could not be observed in individual agents, either human or LLM-based.

Overcoming these two limitations requires next-generation modeling frameworks that account for complex LLM–human interactions and which are capable of capturing the complexity of LLMs as cognitive agents.

This Special Issue aims to bring together quantitative, innovative research in this field. We are open to a variety of publication types, including reviews and theoretical papers, empirical research, computational modeling, and Big Data analyses regarding information networks that feature multiple interactions between LLMs or with humans.

Potential topics include, but are not limited to, the following:

  • Interpretable AI for information processing;
  • Models of network science and LLMs for understanding information flow;
  • Models of knowledge construction and representation in LLM and human systems;
  • Complex systematic approaches to knowledge/information modeling;
  • Trustworthy social and sociable interactions;
  • AI systems versus human social media;
  • AI-based techno-social systems;
  • AI-powered social simulations and agent-based modeling.

Prof. Dr. Massimo Stella
Prof. Dr. Giulio Rossetti
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • artificial intelligence
  • interpretable AI
  • human-centric AI
  • large language models
  • complex networks
  • network science
  • knowledge modeling
  • data mining
  • intelligent systems

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Published Papers (1 paper)

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Research

21 pages, 655 KiB  
Article
A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
by Albin Uruqi, Iosif Viktoratos and Athanasios Tsadiras
Future Internet 2025, 17(8), 360; https://doi.org/10.3390/fi17080360 - 8 Aug 2025
Viewed by 336
Abstract
The cold-start problem remains a critical challenge in personalized advertising, where users with limited or no interaction history often receive suboptimal recommendations. This study introduces a novel, three-stage framework that systematically integrates transformer architectures and large language models (LLMs) to improve recommendation accuracy, [...] Read more.
The cold-start problem remains a critical challenge in personalized advertising, where users with limited or no interaction history often receive suboptimal recommendations. This study introduces a novel, three-stage framework that systematically integrates transformer architectures and large language models (LLMs) to improve recommendation accuracy, transparency, and user experience throughout the entire advertising pipeline. The proposed approach begins with transformer-enhanced feature extraction, leveraging self-attention and learned positional encodings to capture deep semantic relationships among users, ads, and context. It then employs an ensemble integration strategy combining enhanced state-of-the-art models with optimized aggregation for robust prediction. Finally, an LLM-driven enhancement module performs semantic reranking, personalized message refinement, and natural language explanation generation while also addressing cold-start scenarios through pre-trained knowledge. The LLM component further supports diversification, fairness-aware ranking, and sentiment sensitivity in order to ensure more relevant, diverse, and ethically grounded recommendations. Extensive experiments on DigiX and Avazu datasets demonstrate notable gains in click-through rate prediction (CTR), while an in-depth real user evaluation showcases improvements in perceived ad relevance, message quality, transparency, and trust. This work advances the state-of-the-art by combining CTR models with interpretability and contextual reasoning. The strengths of the proposed method, such as its innovative integration of components, empirical validation, multifaceted LLM application, and ethical alignment highlight its potential as a robust, future-ready solution for personalized advertising. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)
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