@article {878, title = {The Evolution of the Digital Parliament: Enabling Technologies, Research Gaps, and Future Directions}, journal = {Information}, volume = {17}, year = {2026}, pages = {633}, abstract = {

The evolution of digital technologies is reshaping parliaments worldwide, driving fundamental changes in their operations. Parliaments, being traditionally conservative institutions, typically lean toward {\textquotedblleft}mature{\textquotedblright} emerging or disruptive technologies through cautious, incremental digital transformation attempts, resulting in complex digital parliamentary environments for their users, processes, systems, and tools. The paper employs an integrative literature review as its methodological tool, examining the concept of the {\textquotedblleft}digital parliament{\textquotedblright} and the technologies that enable it. Using a PRISMA-informed methodology as a guide, we conducted an integrative review covering the period 2006{\textendash}2025, and in this context, we retrieved 535 publications, screened 260, thoroughly examined 57, and analyzed and synthesized 34 studies addressing digital parliamentary technologies, digital platforms, and cooperative workspaces. We found that while specific parliamentary technology (ParlTech) applications{\textemdash}including big data analytics, artificial intelligence (AI), and hybrid parliamentary tools{\textemdash}are reaching institutional maturity, the concept of a digital parliament remains fragmented, lacking a unified definitional and operational framework. Key research gaps have been identified concerning user classification, the digitization of parliamentary functions, operations, and processes, as well as the institutionalization of cooperation platforms. Based on these findings, we propose strategic directions toward establishing a responsible, inclusive, and evidence-based digital parliament. This research contributes as a guideline for parliamentary organizations seeking to create, retain, and disseminate public value through the responsible adoption of emerging digital technologies.

}, keywords = {cooperative technology platforms, digital institutionalization, digital parliament, digital transformation, hybrid, parliamentary digital environment, ParlTech, PRISMA}, issn = {2078-2489}, doi = {https://doi.org/10.3390/info17070633}, author = {Koryzis, Dimitris and Spiliotopoulos, Dimitris and Margaris, Dionisis and Vassilakis, Costas and Fitsilis, Fotios} } @article {879, title = {Large Language Models as Explainable AI Ensemble Aggregators for Business Review Sentiment Analysis: A Comparative Study with Classical Ensembles}, journal = {Applied Sciences}, volume = {16}, year = {2026}, pages = {6479}, abstract = {

Online business reviews encode rich customer sentiment that is critical for commercial decision making, yet accurately predicting star ratings from free text remains a challenging five-class classification problem. Classical ensemble methods{\textemdash}Soft Voting, Weighted Voting, and Stacking{\textemdash}aggregate complementary base-model outputs to improve predictive performance, but they produce opaque decisions that are unintelligible to business stakeholders. This paper proposes using a large language model (LLM), specifically unsloth/LLaMA-3.3-70B-Instruct, as an Explainable AI (XAI) ensemble aggregator: the LLM receives the predictions and confidence scores of four heterogeneous base models (Logistic Regression, Support Vector Machine, Na{\"\i}ve Bayes, and BERT-base-uncased) and reasons over them to produce both a final star-rating prediction and a natural-language explanation. We evaluate the full pipeline on 10,000-sample balanced and natural-distribution test sets derived from the Yelp Academic Dataset, with additional cross-lingual validation on Spanish Amazon Reviews. The LLM aggregator (LLAMA_AGG) achieves the highest macro-F1 on both pipelines (0.6800 on balanced; 0.6720 on natural) and the best ordinal calibration (QWK = 0.9111 on balanced; 0.9337 on natural), outperforming all classical aggregators and base models. A detailed Explainable AI analysis reveals that the LLM revises 28.07\% of its standalone predictions after observing the ensemble outputs, improving the accuracy by +22.2 percentage points on the revised cases. The aggregator corrects severe polar bias in the standalone LLM ({\textpm}0.35 recall improvement on mid-range star classes) and produces longer explanations when evidence is conflicted{\textemdash}a quantitative signal of deliberative reasoning. A formal human evaluation with two judges confirms high explanation faithfulness (4.47/5) and readability (4.82/5). Model scale ablation shows an 8B parameter variant achieves 90.8\% agreement with the 70B model, enabling practical deployment. These findings demonstrate that Explainable AI can be achieved through LLM-based ensemble aggregation, establishing a principled approach for business-review sentiment analysis.

}, keywords = {BERT, business reviews, chain-of-thought, ensemble learning, Explainable AI, large language models, Llama, meta-reasoning, Natural language processing, Sentiment Analysis, Soft Voting, Stacking, star rating prediction, Yelp}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app16136479}, url = {https://www.mdpi.com/2076-3417/16/13/6479}, author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @article {868, title = {A Large-Scale Empirical Study of LLM Orchestration and Ensemble Strategies for Sentiment Analysis in Recommender Systems}, journal = {Future Internet}, volume = {18}, year = {2026}, pages = {112}, abstract = {

This paper presents a comprehensive empirical evaluation comparing meta-model aggregation strategies with traditional ensemble methods and standalone models for sentiment analysis in recommender systems beyond standalone large language model (LLM) performance. We investigate whether aggregating multiple LLMs through a reasoning-based meta-model provides measurable performance advantages over individual models and standard statistical aggregation approaches in zero-shot sentiment classification. Using a balanced dataset of 5000 verified Amazon purchase reviews (1000 reviews per rating category from 1 to 5 stars, sampled via two-stage stratified sampling across five product categories), we evaluate 12 different leading pre-trained LLMs from four major providers (OpenAI, Anthropic, Google, and DeepSeek) in both standalone and meta-model configurations. Our experimental design systematically compares individual model performance against GPT-based meta-model aggregation and traditional ensemble baselines (majority voting, mean aggregation). Results show statistically significant improvements (McNemar{\textquoteright}s test, p \< 0.001): the GPT-5 meta-model achieves 71.40\% accuracy (10.15 percentage point improvement over the 61.25\% individual model average), while the GPT-5 mini meta-model reaches 70.32\% (9.07 percentage point improvement). These observed improvements surpass traditional ensemble methods (majority voting: 62.64\%; mean aggregation: 62.96\%), suggesting potential value in meta-model aggregation for sentiment analysis tasks. Our analysis reveals empirical patterns including neutral sentiment classification challenges (3-star ratings show 64.83\% failure rates across models), model influence hierarchies, and cost-accuracy trade-offs ($130.45 aggregation cost vs. $0.24{\textendash}$43.97 for individual models per 5000 predictions). This work provides evidence-based insights into the comparative effectiveness of LLM aggregation strategies in recommender systems, demonstrating that meta-model aggregation with natural language reasoning capabilities achieves measurable performance gains beyond statistical aggregation alone.

}, keywords = {empirical evaluation, ensemble learning, large language models, LLM orchestration, meta-model aggregation, multi-model aggregation, reasoning models, Recommender Systems, sentiment classification, zero-shot sentiment analysis}, issn = {1999-5903}, doi = {https://doi.org/10.3390/fi18020112}, url = {https://www.mdpi.com/1999-5903/18/2/112}, author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @conference {874, title = {A Multimodal Pipeline for Personalized Recommendations from Greek YouTube Content: Integrating Automatic Speech Recognition and Aspect-Based Sentiment Analysis}, booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications}, year = {2026}, publisher = {IEEE Xplore}, organization = {IEEE Xplore}, abstract = {

Audio/video social media and video sharing sites like YouTube allow users to create their own community where they post audio content. This type of content includes product reviews which consumers use to assist others in making buying decisions, and this content can be processed using NLP and automated Aspect-Based Sentiment Analysis (ABSA) to produce input for recommender systems. However, developing these systems for low resource languages (such as Modern Greek) lags behind significantly. To address this gap, in this paper we introduce a complete ABSA framework, which transforms Greek multimedia reviews into structured and accurate data, to be used for personalized product recommendation. The proposed framework uses a multistage pipeline to address the complexities of spoken text, such as the highly inflected morphology of the Greek language and slang utilized in reviewing. The first stage of the pipeline transforms raw audio data into a form suitable for processing, while the second stage applies linguistic processing. Two alternatives were evaluated for this stage, where the first utilizes a native Greek Sentiment Lexicon and the second firstly applies Greek-to-English automated translation, followed by English Language Sentiment Analysis. The efficacy of the proposed framework has been assessed through an experiment in which each of the pipeline outcomes is compared against independent human assessments. The assessment shows that the translation-based approach achieves high accuracy and alignment with expert human raters. The proposed work also takes into account the effect of {\textquotedblleft}noise{\textquotedblright} owing to Automatic Speech Recognition (ASR) errors and studies the propagation of these errors across 12 distinct product attributes. The proposed pipeline thus constitutes an effective solution for exploiting audiovisual content in recommender systems to generate personalized recommendations.

}, keywords = {Aspect-Based Sentiment Analysis (ABSA), Automatic Speech Recognition (ASR), Modern Greek NLP, Multimedia Data Mining, Personalized Recommender Systems, User Preference Modeling}, author = {Singh, Manvir and Roumeliotis, Konstantinos I. and Vassilakis, Costas and Margaris, Dionisis and Mpardis, Giorgos and Spiliotopoulos, Dimitris} } @article {869, title = {Optimizing Collaborative Filtering for Accurate Rating Predictions in Very Sparse Datasets}, journal = {Future Internet}, volume = {18}, year = {2026}, pages = {114}, abstract = {

Collaborative filtering is one of the most widely used methods for user rating prediction in recommender systems. To evaluate a collaborative filtering system, rating datasets are typically used, which comprise thousands to millions of records consisting of user{\textendash}item{\textendash}rating tuples. Initially, a similarity metric is used to quantify the closeness between each user and every other user in the dataset, typically based on the ratings that each pair of users has given to the same items. Subsequently, the K users having the largest similarity to the target user are used to produce rating predictions, which lead to recommendations. A particularly challenging case arises when the rating dataset is very sparse. In this scenario, it is difficult not only to find users with commonly rated items but also to determine the optimal similarity metric and suitable values for variable K. Setting a small value for K results in extremely low prediction coverage, leading to unsuccessful recommendations, while setting a very large K value increases memory requirements and prediction/recommendation generation time. Through a multiparameter experiment, this work aims to determine the optimal settings for rating predictions when very sparse datasets are used in collaborative filtering recommender systems.

}, keywords = {collaborative filtering, K-nearest neighbors (KNN), optimal prediction settings, prediction accuracy, Rating prediction, Recommender Systems, similarity metrics, sparse collaborative filtering, Sparse Datasets}, issn = {1999-5903}, doi = {10.3390/fi18020114}, url = {https://www.mdpi.com/1999-5903/18/2/114}, author = {Lapadaki, Sofia-Anna and Nanos, John and Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris} } @conference {876, title = {Unveiling Structural Distributional Bias in Collaborative Filtering for Ultra-Sparse Datasets}, booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications}, year = {2026}, publisher = {IEEE Xplore}, organization = {IEEE Xplore}, abstract = {

Collaborative Filtering is one of the most widely used rating prediction methods for recommender systems in recent years. It is based on a solid and verified rationale, it supports explainability and usually delivers satisfactory results. However, its performance declines when applied to ultra-sparse datasets. Despite extensive research, the root cause of its failure in such environments still requires further investigation. This work systematically investigates structural distribution biases in CF ultra-sparse datasets, by examining 11 datasets in this category. The findings of this study are threefold. First, this study reveals that the rating distribution within ultra-sparse datasets, exhibits a strong skew toward boundary ratings, thus creating a non-normal rating structure that challenges the traditional Collaborative Filtering algorithms. Secondly, through extensive experiments, employing four different similarity metrics and using three nearest neighbor selection thresholds, it asserts that ratings with values that are underrepresented in the dataset tend to produce significantly higher error margins {\textemdash} up to four times larger than predictions corresponding to the most frequent rating values. The third and most important finding of this study is the phenomenon of mean rating attraction. Specifically, it reveals that the rating predictions are consistently anchored to user and item averages, a process which essentially computationally smooths the rating predictions, instead of recognizing true and unique non-conforming preferences. The above conclusions were found to be independent of both the selection of user similarity metric and the neighbor selection threshold; rather, they are grounded on the inherent structural bias of the input data. This work offers a new perspective for Collaborative Filtering algorithm design and implementation, by identifying structural bias as an important dimension that needs to be handled by rating prediction algorithms, along with the dimension of similarity metrics between users and items.

}, keywords = {collaborative filtering, Performance evaluation, Recommender Systems, similarity metrics, Structural Distributional Bias, Ultra-Sparse datasets}, author = {Nanos, John and Margaris, Dionisis and Mpardis, Giorgos and Roumeliotis, Konstantinos I. and Vassilakis, Costas and Spiliotopoulos, Dimitris} } @article {860, title = {An evaluation review of user similarity metrics in sparse collaborative filtering datasets}, journal = {International Journal of Data Science and Analytics}, year = {2025}, abstract = {

Collaborative filtering (CF) is one of the most prominent recommender system (RecSys) techniques of the recent years. CF generates rating predictions for the items that the user has not evaluated yet, using the evaluations of users with similar likings to the same items. Therefore, in CF the task of finding these users (which can be considered as reliable recommenders) is of high importance, while this task is especially challenging on sparse datasets. To this end, many user similarity metrics have been introduced and used in the literature, such as the Vector (or Cosine) Similarity metric, the Spearman rank correlation, the Pearson Correlation Coefficient (PCC), and others. For a CF RecSys, the use of the most efficient similarity metric is of great importance. This paper assesses the effectiveness of 15 user similarity metrics in sparse CF datasets, by conducting an extensive set of experiments. These experiments include 10 sparse CF datasets with diverse item domains, two neighbour selection approaches, two rating prediction formulas, and three rating prediction accuracy metrics. The evaluation results show that the metrics that achieve the best prediction results are found to be the Spearman rank correlation, followed by the Adjusted Rand Index, the Constrained PCC, and the Chebysev distance. Interestingly, the most widely used similarity metrics in CF research, i.e. the PCC and the Cosine Similarity, are not among the best performing metrics.

}, keywords = {collaborative filtering, Evaluation review, Recommender Systems, Sparse Datasets, User similarity metrics}, issn = {2364-4168}, doi = {10.1007/s41060-025-00846-4}, author = {Sgardelis, Kiriakos and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} }