@conference {875, title = {A Comparative Study of Qwen and Gemma Large Language Models for Social Media Slang and Emoji Sentiment Analysis for Business Recommender Systems}, booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications}, year = {2026}, publisher = {IEEE Xplore}, organization = {IEEE Xplore}, abstract = {

Sentiment analysis of social media text remains a fundamentally challenging task due to the pervasive use of informal slang, abbreviations, deliberate spelling modifications, and emojis. Traditional lexicons and early machine learning models frequently struggle to decode the nuanced semantics of these modern digital expressions, particularly when distinguishing between genuine sentiment and sarcasm. In this paper, we present a comprehensive comparative study investigating the capabilities of two recent 4-billion-parameter large language models (LLMs), Qwen3.5 (4B) and Gemma3 (4B), in robust social media sentiment analysis. The core contribution of our study is the fine-tuning of these models at a comparable scale using 4-bit NormalFloat (NF4) quantization combined with the Unsloth library, establishing a highly efficient and reproducible training pipeline. We critically evaluate both models in zero-shot and fine-tuned settings on a diverse, emoji-heavy dataset characterized by four complex sentiment classes: Positive, Negative, Neutral, and Sarcastic. Our experimental results demonstrate that while both models show highly capable zero-shot generalization (Qwen achieving 75.95\% accuracy and Gemma achieving 73.20\%), they exhibit distinct failure modes surrounding sarcastic interpretations. However, alignment via Low-Rank Adaptation (LoRA) dramatically elevates their performance, allowing both to achieve 100\% accuracy on the held-out test set while maintaining distinctly competitive inference speeds suitable for real-time applications. We also provide an exhaustive architectural analysis of the models{\textquoteright} inference latency, per-class prediction bottlenecks, and training efficiency, highlighting practical considerations and trade-offs for deploying lightweight LLMs in real-world sentiment analysis environments. The strong inference-speed profile of both fine-tuned models makes them directly deployable as opinion-mining modules within business recommender system pipelines, where timely and accurate decoding of customer-expressed sentiment{\textemdash}including sarcasm{\textemdash}translates into richer preference signals and more reliable personalization.

}, keywords = {Gemma, large language models, LoRA, Quantization, Qwen, Sentiment Analysis, social media, Unsloth}, author = {Konstantinos I. Roumeliotis and Dimitris Spiliotopoulos and Christos Tryfonopoulos and Dionisis Margaris and Giorgos Mpardis and Costas Vassilakis} } @conference {877, title = {The enhancement of Passenger Ground Transfer Management by the BPMN modeling and Real-Time Flight Data}, booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications}, year = {2026}, publisher = {IEEE Xplore}, organization = {IEEE Xplore}, abstract = {

Passenger ground transfer involves managing the movement of fleets of vehicles to shuttle passengers between airports and hotels. A persistent challenge and source of operational inefficiency in this environment is deadheading - driving an empty vehicle to pick up passengers. In this paper, we describe a Decision Support System (DSS) to optimally assign transfer routes. The DSS is modeled using BPMN 2.0 and uses real-time flight data, regulatory constraints, and geo-specific information to minimize the number of deadheading kilometers driven. The decision engine provides optimized driver assignment suggestions (ordered by the best option), which the dispatchers can then accept or reject. We validated our system with a real-world dataset of 987,342 transfers collected from a commercial service operating 734 transfer companies in Greece, Cyprus, and Belgium, where 3,700 vehicles and 5,400 drivers were registered. We found that the number of deadheading kilometers per transfer was reduced by 23.8\%, the transfer cost decreased by 23.8\%, driver utilization increased by 7.2 percentage points, and customer waiting times for transfers in the case of a flight delay decreased by 45.2\%. The use of the DSS also reduced the dispatcher workload (on average by an estimated 5.1 hours per day) by offering automated suggestions.

}, keywords = {BPMN, Business Process Modeling, Deadheading Minimization, Decision Support System, Key Performance Indicators, Passenger Ground Transfer, Real-Time Flight Data, Route Optimization}, author = {Giorgos Mpardis and Dionisis Margaris and Konstantinos I. Roumeliotis and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {873, title = {Optimizing Collaborative Filtering in Very Sparse Datasets: a Confidence-based Approach}, booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications}, year = {2026}, publisher = {IEEE Xplore}, organization = {IEEE Xplore}, abstract = {

Collaborative filtering systems are a key tool for personalized recommendations, predicting user preferences based on historical data. A major challenge in collaborative filtering is the reliability of predictions, particularly in highly sparse datasets, where the limited overlap of common ratings between users impedes the identification of reliable neighbors, reducing both the accuracy and coverage of the predictions. So far, the literature has proposed confidence factors to quantify prediction reliability, typically using the Pearson and Cosine similarities with threshold-based neighbor selection. However, a recent study demonstrated that using the Sigmoid Cosine similarity metric in combination with the KNN neighborhood selection method and adapting the value of K to each level of dataset sparsity, significantly improves prediction performance. In this paper, we revisit the calculation of confidence factors under these improved settings, assessing their validity and effectiveness across datasets with varying sparsity levels. Continuous assessment of collaborative filtering methods and adjustment of their parameters according to dataset characteristics is particularly crucial in highly sparse datasets, where the reliability of neighbors strongly influences prediction quality.

}, keywords = {collaborative filtering, Confidence Factors, Optimization, Rating Predictions, Recommender Systems, Sparse Datasets}, author = {Vassiliki Stouraiti and Giorgos Mpardis and Konstantinos I. Roumeliotis and Costas Vassilakis and Christos Tryfonopoulos and Dimitris Spiliotopoulos} }