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2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications
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2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 7856

Special Issue Editors

Prof. Dr. Costas Vassilakis

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of the Peloponnese, Akadimaikou G.K. Vlachou, 22100 Tripoli, Greece
Interests: information systems; recommender systems; semantic web technologies and applications; cultural informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Dionisis Margaris

E-Mail Website
Guest Editor
Department of Digital Systems, University of the Peloponnese, 23100 Kladas, Greece
Interests: recommender systems; software; personalization; web services; business processes; social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, recommender systems are indispensable in most personalized systems implementing information access and content delivery, supporting a great variety of user activities. Recommender systems alleviate the problem of information overload, identifying and promoting content that is deemed more suitable for each individual user. To this end, recommender systems collect and process information regarding user preferences, likings, and previous actions; the user’s current context (such as the user’s location or company, the time of day or week, etc.); the user’s neighborhood and activity in social networks (friends, posts, message exchanges, and so forth); the characteristics of items to be recommended, including semantic information; and so on. Both static and dynamic views of the collected data are considered, and the algorithms employed to process the available data range from collaborative filtering and statistical models to knowledge-based approaches and matrix factorization.

This Special Issue on “2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications” aims to promote new theoretical models, approaches, algorithms, and applications related to the area of recommender systems. Authors should submit papers describing significant, original, and unpublished work. Possible topics include, but are not limited to, the following:

  • Models and algorithms to improve recommendation quality.
  • Recommendation algorithms that exploit contextual information, social network information, and/or rich item descriptions.
  • Techniques and methods for enhancing recommender system performance in the context of big data.
  • Privacy-preserving techniques for recommender systems.
  • Novel recommender system applications.
  • Case studies of real-world implementations.
  • Algorithm scalability, performance, and implementations.
  • Cross-disciplinary approaches involving recommender systems.
  • AI-based and explainable recommendations.

Prof. Dr. Vassilakis Costas
Dr. Dionisis Margaris
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 250 words) can be sent to the Editorial Office for assessment.

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. Information 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 1800 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
  • contextual information
  • social networks
  • item semantics
  • big data and performance
  • privacy preservation
  • AI-based recommender systems
  • explainable recommendations

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Related Special Issue

Published Papers (8 papers)

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Research

16 pages, 1766 KB  
Article
A Hybrid Recommendation Approach for Adaptive Worksheet Generation Using Pedagogically Structured Learning Objects
by Iraklis Katsaris, Sakellaris Sfakiotakis, Ilias Logothetis and Nikolas Vidakis
Information 2026, 17(5), 437; https://doi.org/10.3390/info17050437 - 1 May 2026
Viewed by 306
Abstract
Adaptive recommendation mechanisms are widely used to personalise digital learning environments; however, many existing approaches prioritise algorithmic optimisation while providing limited insight into how recommendation behaviour aligns with pedagogically structured instructional artefacts, such as worksheets. To address this gap, this paper proposes a [...] Read more.
Adaptive recommendation mechanisms are widely used to personalise digital learning environments; however, many existing approaches prioritise algorithmic optimisation while providing limited insight into how recommendation behaviour aligns with pedagogically structured instructional artefacts, such as worksheets. To address this gap, this paper proposes a hybrid recommendation approach for adaptive worksheet generation that integrates content-based and collaborative filtering with explicit pedagogical constraints derived from Bloom’s Revised Taxonomy. The system ranks and selects learning and evaluation objects across cognitive levels by combining learner profiles, behavioural signals, and similarity-based information within a unified scoring framework. A simulation-based evaluation was conducted to examine the internal behaviour, stability, and instructional alignment of the recommendation engine under controlled conditions, using Bloom-aligned worksheets and synthetic learner profiles. The analysis focuses on expected–actual alignment and adaptive variation across cognitive levels rather than learning outcomes. Results indicate strong alignment with the intended instructional structure at lower cognitive levels, while bounded and interpretable adaptive variation emerges at higher levels. Evaluation object recommendations showed high agreement with the instructional design, exceeding 95% across simulated conditions. Overall, the study demonstrates how hybrid recommendation mechanisms can support adaptive content selection in pedagogically structured learning scenarios, offering a transparent and robust foundation for information-driven educational systems. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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16 pages, 2714 KB  
Article
Mitigating Distribution Shift in Offline RL-Based Recommender Systems with a Q-Learning Regularization Decision Transformer
by Yu Zhou, Xinyu Guo, Yuanbo Jiang, Jiaxuan Fang, Jin-Qiang Wang, Peng Zhi, Gang Liu, Rui Zhou, Ling-Huey Li, Chuanyi Liu, Qingguo Zhou and Kuan-Ching Li
Information 2026, 17(4), 364; https://doi.org/10.3390/info17040364 - 13 Apr 2026
Viewed by 729
Abstract
Optimizing long-term user satisfaction in sequential recommender systems is a critical challenge. Offline reinforcement learning (RL) offers a promising solution by learning recommendation policies from historical interaction logs without incurring the high costs of online exploration. However, offline RL suffers from severe distribution [...] Read more.
Optimizing long-term user satisfaction in sequential recommender systems is a critical challenge. Offline reinforcement learning (RL) offers a promising solution by learning recommendation policies from historical interaction logs without incurring the high costs of online exploration. However, offline RL suffers from severe distribution shift: the learned policy often overestimates the value of out-of-distribution (OOD) items, leading to unreliable recommendations and compromising user satisfaction. To address this issue, we propose a novel framework known as the Q-Learning Regularized Decision Transformer (QRDT). Built upon the Decision Transformer architecture, QRDT models recommendations as a sequence prediction task to capture complex user interest dynamics. To mitigate distribution shift, the QRDT integrates Kullback–Leibler (KL) divergence and maximum entropy regularization into the Q-value function, enabling conservative long-term value estimation while encouraging diverse exploration within the logged data distribution. Extensive experiments on four real-world Amazon e-commerce datasets (CDs, Clothing, Cellphones, and Beauty) demonstrate that the QRDT achieves competitive performance and outperforms the PGPR baseline in most scenarios. Specifically, the proposed method yields improvements of 2.99% in Hit Rate (HR), 2.19% in Normalized Discounted Cumulative Gain (NDCG), 0.94% in Recall, and 0.84% in Precision, verifying the effectiveness of our regularization approach. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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32 pages, 2911 KB  
Article
End-to-End Personalization via Unifying LLM Agents and Graph Attention Networks for Entertainment Recommendation
by Danial Ebrat, Sepideh Ahmadian and Luis Rueda
Information 2026, 17(4), 344; https://doi.org/10.3390/info17040344 - 2 Apr 2026
Viewed by 1109
Abstract
Recommender systems are central to helping users navigate the rapidly expanding entertainment ecosystem, yet achieving strong personalization with limited feedback while maintaining interpretability remains difficult, particularly under cold-start conditions and heterogeneous item metadata. This work presents an end-to-end hybrid recommendation framework that unifies [...] Read more.
Recommender systems are central to helping users navigate the rapidly expanding entertainment ecosystem, yet achieving strong personalization with limited feedback while maintaining interpretability remains difficult, particularly under cold-start conditions and heterogeneous item metadata. This work presents an end-to-end hybrid recommendation framework that unifies a Large Language Model (LLM) with Graph Attention Network (GAT)-based collaborative filtering to improve both ranking accuracy and explanation quality across movies, books, and music. LLM-based agents first transform raw metadata such as titles, genres, descriptions, and auxiliary attributes into semantically grounded user and item profiles, which are embedded and used as initial node features in a user–item bipartite graph processed by a GAT-based recommender. Model optimization relies on a hybrid objective combining Bayesian Personalized Ranking, cosine-similarity regularization, and robust negative sampling to better align semantic and collaborative signals. Finally, in the post-processing stage, an LLM-based agent re-ranks the GAT outputs using a proposed Hybrid Confidence-Weighted Binary Search Tree, and another LLM-based agent that produces natural-language justifications tailored to each user. Experiments on diverse benchmark datasets and extensive ablations demonstrate that the proposed methodology increases precision, recall, NDCG, and MAP across various values of K. In addition, the post processing step is especially effective in cold-start scenarios, consistently strengthening recommendation metrics and enhancing transparency at smaller values of K. Overall, integrating LLM-enriched representations with attention-based graph modeling enables more accurate and explainable entertainment recommendations. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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17 pages, 6684 KB  
Article
Modeling the Spreading of Fake News Through the Interactions Between Human Heuristics and Recommender Systems
by Franco Bagnoli, Tijan Juraj Cvetković, Andrea Guazzini, Pietro Lió and Riccardo Romei
Information 2026, 17(4), 314; https://doi.org/10.3390/info17040314 - 24 Mar 2026
Viewed by 588
Abstract
In many cases, the pieces of information at our disposal (messages) come from a recommender source, which can be either an official news system, a large language model or simply a social network. Often, also, these messages are build so as to promote [...] Read more.
In many cases, the pieces of information at our disposal (messages) come from a recommender source, which can be either an official news system, a large language model or simply a social network. Often, also, these messages are build so as to promote their active spreading, which, on the other hand, has a positive effect on one’s own popularity. However, the content of the message can be false, giving origin to a phenomenon analogous to the spreading of a disease. In principle, there is always the possibility of checking the correctness of the message by “investing” some time, so we can say that this checking has a cost. We develop a simple model based on the mechanism of “risk perception” (propensity to check the falseness of a message) and mutual trustability (affinity), based on the average number of fake messages received and checked. On the other side, the probability of emitting a fake message is inversely proportional to one’s risk perception and the affinity among agents is also exploited by the recommender system. We aim at investigating this process with the goal of deriving methods for identifying the penetration level of fake news from behavioral patterns of users. This model represents an integration of cognitive psychology with computational agent-based modeling. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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18 pages, 5321 KB  
Article
Unlikely Pairs: A Decision-Support Recommendation Pipeline for Discovering Semantically Plausible Research Collaborations
by Jorge Galán-Mena, Martín López-Nores, Daniel Pulla-Sánchez, Luis Fernando Guerrero-Vásquez and Juan Pablo Salgado-Guerrero
Information 2026, 17(3), 254; https://doi.org/10.3390/info17030254 - 3 Mar 2026
Viewed by 498
Abstract
Scientific collaboration is increasingly needed to address complex research challenges, yet identifying promising partners in the absence of prior co-authorship remains difficult. We present a decision-support pipeline for discovering researchers who have not previously worked together and whose collaboration is unlikely to emerge [...] Read more.
Scientific collaboration is increasingly needed to address complex research challenges, yet identifying promising partners in the absence of prior co-authorship remains difficult. We present a decision-support pipeline for discovering researchers who have not previously worked together and whose collaboration is unlikely to emerge without deliberate intervention or institutional incentives. The approach leverages document-level semantic representations to estimate proximity between publications, aggregates these similarities at the author level, and surfaces collaboration opportunities that are not evident from the co-authorship graph. To support interpretation by decision makers, a separate LLM module proposes potential joint research directions, which are subsequently annotated with multi-label fields of study. We evaluate the pipeline through an institutional case study, analyzing 7531 publications from 2009 to 2024 using retrospective, temporally shifted windows. While only a small fraction of suggested pairs materialized spontaneously in subsequent periods, the collaborations that do emerge exhibit strong semantic alignment with the computed recommendations (high cosine similarity) and substantial thematic overlap. These results indicate that semantic proximity can act as an early indicator of latent complementarity between researchers without prior ties, supporting intentional institutional mediation and complementing topology-driven approaches that predict links under passive evolution. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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27 pages, 2766 KB  
Article
Explainable Reciprocal Recommender System for Affiliate–Seller Matching: A Two-Stage Deep Learning Approach
by Hanadi Almutairi and Mourad Ykhlef
Information 2026, 17(1), 101; https://doi.org/10.3390/info17010101 - 19 Jan 2026
Viewed by 806
Abstract
This paper presents a two-stage explainable recommendation system for reciprocal affiliate–seller matching that uses machine learning and data science to handle voluminous data and generate personalized ranking lists for each user. In the first stage, a representation learning model was trained to create [...] Read more.
This paper presents a two-stage explainable recommendation system for reciprocal affiliate–seller matching that uses machine learning and data science to handle voluminous data and generate personalized ranking lists for each user. In the first stage, a representation learning model was trained to create dense embeddings for affiliates and sellers, ensuring efficient identification of relevant pairs. In the second stage, a learning-to-rank approach was applied to refine the recommendation list based on user suitability and relevance. Diversity-enhancing reranking (maximal marginal relevance/explicit query aspect diversification) and popularity penalties were also implemented, and their effects on accuracy and provider-side diversity were quantified. Model interpretability techniques were used to identify which features affect a recommendation. The system was evaluated on a fully synthetic dataset that mimics the high-level statistics generated by affiliate platforms, and the results were compared against classical baselines (ALS, Bayesian personalized ranking) and ablated variants of the proposed model. While the reported ranking metrics (e.g., normalized discounted cumulative gain at 10 (NDCG@10)) are close to 1.0 under controlled conditions, potential overfitting, synthetic data limitations, and the need for further validation on real-world datasets are addressed. Attributions based on Shapley additive explanations were computed offline for the ranking model and excluded from the online latency budget, which was dominated by approximate nearest neighbors-based retrieval and listwise ranking. Our work demonstrates that high top-K accuracy, diversity-aware reranking, and post hoc explainability can be integrated within a single recommendation pipeline. While initially validated under synthetic evaluation, the pipeline was further assessed on a public real-world behavioral dataset, highlighting deployment challenges in affiliate–seller platforms and revealing practical constraints related to incomplete metadata. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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26 pages, 1823 KB  
Article
Community-Aware Two-Stage Diversification for Social Media User Recommendation with Graph Neural Networks
by Soh Yoshida
Information 2026, 17(1), 29; https://doi.org/10.3390/info17010029 - 31 Dec 2025
Viewed by 1005
Abstract
The occurrence of filter bubbles and echo chambers in social media recommendation systems poses a significant threat to information diversity and democratic discourse. Although graph neural networks (GNNs) achieve leading accuracy in user recommendation, their optimization for engagement metrics inadvertently reinforces homophily, creating [...] Read more.
The occurrence of filter bubbles and echo chambers in social media recommendation systems poses a significant threat to information diversity and democratic discourse. Although graph neural networks (GNNs) achieve leading accuracy in user recommendation, their optimization for engagement metrics inadvertently reinforces homophily, creating isolated information ecosystems. This research developed community-aware two-stage diversification with GNNs (CATD-GNN), a method that leverages the inherent community structure of social networks to promote diversity without sacrificing recommendation quality. CATD-GNN integrates community detection with GNN learning through a two-stage diversification process. The proposed method employs the Louvain method to identify community structures as pseudo-categories, then applies submodular neighbor selection and community-based loss reweighting during GNN training (Stage 1), followed by coverage and redundancy-aware reranking (Stage 2). Twitter data capturing Black Lives Matter discourse and Reddit political discussion networks were used to evaluate the method. CATD-GNN achieves improvements in diversity metrics while maintaining competitive accuracy. The two-stage architecture demonstrates a synergistic effect: the combination of diversity-aware training and coverage-based reranking produces greater improvements than either component alone. The proposed method successfully identifies and recommends users from different communities while preserving recommendation relevance. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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17 pages, 2534 KB  
Article
Modeling Recommender Systems Using Disease Spread Techniques
by Peixiong He, Libo Sun, Xian Gao, Yi Zhou and Xiao Qin
Information 2025, 16(8), 687; https://doi.org/10.3390/info16080687 - 13 Aug 2025
Viewed by 1265
Abstract
Recommender systems on digital platforms profoundly influence user behavior through content dissemination, and their diffusion process is similar to the spreading mechanism of infectious diseases to some extent. In this paper, we use a network-based susceptibility-infection (SI) model to model the propagation dynamics [...] Read more.
Recommender systems on digital platforms profoundly influence user behavior through content dissemination, and their diffusion process is similar to the spreading mechanism of infectious diseases to some extent. In this paper, we use a network-based susceptibility-infection (SI) model to model the propagation dynamics of recommended content, and systematically compare the differences in propagation efficiency among three recommendation strategies based on popularity, collaborative filtering, and content. We constructed scale-free user networks based on real-world clickstream data and dynamically adapted the SI model to reflect the realistic scenario of user engagement decay over time. To enhance the understanding of the recommendation process, we further simulate the visualization changes of the propagation process to show how the content spreads among users. The experimental results show that collaborative filtering performs superior in the initial dissemination, but its dissemination effect decays rapidly over time and is weaker than the other two methods. This study provides new ideas for modeling and understanding recommender systems from an epidemiological perspective. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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