@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 = {<p>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 \&lt; 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.</p>
},
	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 {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 = {<p>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.</p>
},
	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}
}
