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Applied Sciences
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Advanced Models and Algorithms for Recommender Systems
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Advanced Models and Algorithms for Recommender Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2580

Special Issue Editor

Dr. Pavlos Kefalas

E-Mail Website
Guest Editor
Data Engineering Laboratory, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: artificial intelligence; recommender systems; machine learning; information retrieval; data science; intelligent systems; algorithms; databases; large language models

Special Issue Information

Dear Colleagues,

As digital ecosystems grow and diversify, recommender systems have become integral in delivering personalized experiences, enhancing user engagement across sectors like e-commerce, social media, entertainment, and healthcare. With the rapid advancement of AI and data science, there is a growing interest in developing models and algorithms that not only improve recommendation accuracy but also address challenges in scalability, interpretability, user privacy, ethics, cold-start problems, data heterogeneity, and the integration of large language models.

Traditional recommendation approaches, though widely used, face limitations in handling sparse, noisy, and evolving data environments. Recently, advanced techniques in deep learning, graph neural networks, and hybrid collaborative methods have shown promising potential in tackling these challenges by leveraging contextual, sequential, and multi-modal data.

This Special Issue, “Advanced Models and Algorithms for Recommender Systems”, brings together cutting-edge research focused on refining recommender systems through enhanced modeling, innovative algorithms, and practical implementations. Our aim is to provide a comprehensive resource for researchers and practitioners seeking to develop intelligent and reliable recommender systems in increasingly complex digital ecosystems.

Dr. Pavlos Kefalas
Guest Editor

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.

Keywords

  • recommender systems
  • personalization
  • scalability
  • interpretability
  • user privacy
  • cold-start problem
  • data heterogeneity
  • large language models in recommendations
  • deep learning in recommendations
  • graph neural networks
  • hybrid collaborative filtering
  • context-aware recommendations
  • sequential recommendations
  • multi-modal data integration
  • fairness and transparency in recommendations
  • real-world applications
  • artificial intelligence
  • machine learning
  • data science
  • information retrieval
  • algorithms

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Published Papers (4 papers)

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Research

23 pages, 439 KiB  
Article
Evaluating Proprietary and Open-Weight Large Language Models as Universal Decimal Classification Recommender Systems
by Mladen Borovič, Eftimije Tomovski, Tom Li Dobnik and Sandi Majninger
Appl. Sci. 2025, 15(14), 7666; https://doi.org/10.3390/app15147666 - 8 Jul 2025
Viewed by 253
Abstract
Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claude models (3-Haiku and 3.5-Haiku), Gemini [...] Read more.
Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claude models (3-Haiku and 3.5-Haiku), Gemini series (1.0-Pro, 1.5-Flash, and 2.0-Flash), and Llama, Gemma, Mixtral, and DeepSeek architectures. Models were evaluated zero-shot on 900 English and Slovenian academic theses manually classified by professional librarians. Classification prompts utilized the RISEN framework, with evaluation using Levenshtein and Jaro–Winkler similarity, and a novel adjusted hierarchical similarity metric capturing UDC’s faceted structure. Proprietary systems consistently outperformed open-weight alternatives by 5–10% across metrics. GPT-4o achieved the highest hierarchical alignment, while open-weight models showed progressive improvements but remained behind commercial systems. Performance was comparable between languages, demonstrating robust multilingual capabilities. The results indicate that LLM-powered recommender systems can enhance library classification workflows. Future research incorporating fine-tuning and retrieval-augmented approaches may enable fully automated, high-precision UDC assignment systems. Full article
(This article belongs to the Special Issue Advanced Models and Algorithms for Recommender Systems)
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22 pages, 989 KiB  
Article
A Second-Classroom Personalized Learning Path Recommendation System Based on Large Language Model Technology
by Qiankun Yang and Changyong Liang
Appl. Sci. 2025, 15(14), 7655; https://doi.org/10.3390/app15147655 - 8 Jul 2025
Viewed by 440
Abstract
To address the limitations of existing learning path recommendation methods—such as poor adaptability, weak personalization, and difficulties in processing long sequences of student behavior and interest data—this paper proposes a personalized learning path recommendation system for the second classroom based on large language [...] Read more.
To address the limitations of existing learning path recommendation methods—such as poor adaptability, weak personalization, and difficulties in processing long sequences of student behavior and interest data—this paper proposes a personalized learning path recommendation system for the second classroom based on large language model (LLM) technology, with a focus on integrating the pre-trained model GPT-4. The goal is to improve recommendation accuracy and personalization by leveraging GPT-4’s strong long-sequence modeling capability. The system fuses students’ multimodal data (e.g., physiological signals, facial expressions, activity levels, and emotional states), extracts deep features using GPT-4, and generates tailored learning paths based on individual feature vectors. It also incorporates incremental learning and self-attention mechanisms to enable real-time feedback and dynamic adjustments. A generative adversarial network (GAN) is introduced to enhance diversity and innovation in recommendations. The experimental results show that the system achieves a personalized recommendation accuracy of over 92%, with coverage and recall rates exceeding 91% and 93%, respectively. Feedback adjustment time remains within 1.5 s, outperforming mainstream models. This study provides a novel and effective technical framework for personalized learning in the second classroom, promoting both efficient resource utilization and student development. Full article
(This article belongs to the Special Issue Advanced Models and Algorithms for Recommender Systems)
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29 pages, 1971 KiB  
Article
Mathematical Model of Data Processing in a Personalized Search Recommendation System for Digital Collections
by Serhii Semenov, Wojciech Baran, Magdalena Andrzejewska, Maxim Pochebut, Inna Petrovska, Oksana Sitnikova, Marharyta Melnyk and Anastasiya Mekhovykh
Appl. Sci. 2025, 15(13), 7583; https://doi.org/10.3390/app15137583 - 6 Jul 2025
Viewed by 349
Abstract
This paper presents a probabilistic-temporal modeling approach for analyzing data processing stages in a personalized recommendation system for digital heritage collections. The methodology is based on (Graphical Evaluation and Review Technique) GERT network formalism, which enables the representation of complex probabilistic workflows with [...] Read more.
This paper presents a probabilistic-temporal modeling approach for analyzing data processing stages in a personalized recommendation system for digital heritage collections. The methodology is based on (Graphical Evaluation and Review Technique) GERT network formalism, which enables the representation of complex probabilistic workflows with feedbacks and alternative branches. For each processing stage, corresponding GERT-schemes were developed, and equivalent transfer functions were derived. Using Laplace transform inversion techniques, probability density functions of processing time were recovered, followed by the calculation of key statistical metrics, including expectation, standard deviation, and quantiles. The results demonstrate that the proposed approach allows for detailed temporal performance evaluation, including the estimation of time exceedance probabilities at each stage. This provides a quantitative basis for optimizing recommendation system design and highlights the applicability of GERT-based modeling to intelligent data-driven services in the cultural domain. Full article
(This article belongs to the Special Issue Advanced Models and Algorithms for Recommender Systems)
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22 pages, 1428 KiB  
Article
Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
by Chao Duan, Jin Yang, Qiaoling Cui, Wenlong Zhang, Xuelian Wan and Mingyan Zhang
Appl. Sci. 2025, 15(8), 4204; https://doi.org/10.3390/app15084204 - 11 Apr 2025
Viewed by 1030
Abstract
Personalized learning resource recommendation is an essential component of intelligent tutoring systems. To address the issue of the plethora of learning resources and enhance the learner experience in intelligent tutoring systems, learning resource recommendation systems have been developed to model learners’ preferences. Despite [...] Read more.
Personalized learning resource recommendation is an essential component of intelligent tutoring systems. To address the issue of the plethora of learning resources and enhance the learner experience in intelligent tutoring systems, learning resource recommendation systems have been developed to model learners’ preferences. Despite numerous efforts and achievements in academia and industry toward more personalized learning, intelligent education tailored to individual learners still faces challenges, such as inadequate user representation and potential information loss during the aggregation of multi-source heterogeneous information features. In recent years, knowledge-graph-based recommendation systems have brought hope for mitigating these issues and achieving more accurate recommendations. In this paper, we propose a novel personalized learning resource recommendation method based on a knowledge graph named the Learner-Enhanced Knowledge Graph Attention (LKGA) network. This model enhances learner representation by extracting collaborative signals, where learning resources clicked by learners who have clicked the same resource are considered potential collaborative signals and are concatenated with the original learning resource features to form the initial entity set for the learner. Furthermore, during the entity aggregation process, each tail entity has different semantic expressions, and an attention mechanism is used to distinguish the importance of different neighbor entities. Additionally, residual connections are added in each hop of the learner’s aggregation process, with the information from the first hop added to each subsequent hop to reduce information loss. We applied the proposed LKGA model to a real-world dataset, and the experimental results fully validate the effectiveness of our model. Full article
(This article belongs to the Special Issue Advanced Models and Algorithms for Recommender Systems)
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