Emre Yalcin

The quest to solve complex, multidimensional, and nonlinear optimization problems frequently encountered in engineering and scientific research has increased interest in heuristic and metaheuristic algorithms. In this study, six new-generation metaheuristic algorithms that have not been previously applied to engineering problems in the literature are introduced and tested on various engineering problems. The Hiking Optimization Algorithm (HOA) models route selection based on slope, simulating the paths followed by individuals during nature hikes. The Fungal Growth Optimization (FGO) algorithm is inspired by the growth and propagation mechanisms of fungi, while the Mirage Simulation Optimization (MSO) seeks solutions based on the exploration-exploitation balance of atmospheric mirages. The Stellar Oscillation Optimization (SOO) mimics the internal oscillations of stars and follows a strategy from global to local search. The mathematically grounded Adaptive Evolutionary Algorithm (AEA) offers an evolutionary learning approach by evaluating potential knowledge in unexplored regions. These algorithms were tested for the first time on six engineering design problems, including a cantilever beam, speed reducer, crashworthiness, multi-disc clutch-brake, gas transmission compressor, and industrial cooling system. The results revealed that the Stellar Oscillation Optimization (SOO) algorithm demonstrated superior performance in terms of average solution quality and convergence speed compared to the other algorithms.

This study introduces a hybrid similarity measure for user-based collaborative filtering that combines traditional rating-based similarities with popularity-aware components to enhance neighborhood selection and prediction accuracy. Items are categorized into popular, diverse, and niche groups using a Pareto-based distribution of user ratings. Probabilistic user profiles are created to capture tendencies toward these categories, and similarities are computed using Jensen-Shannon divergence. These category-based similarities are integrated with Pearson correlation through an adjustable α parameter, addressing sparsity challenges while preserving the precision of rating-based profiles. Experiments on three real-world datasets show that optimal performance is achieved at α=0.9, where rating-based similarities act as the primary driver of accurate predictions, while category-based profiles serve as supportive elements to refine neighborhood selection. The hybrid measure demonstrates significant improvements in MAE and RMSE, particularly in the sparsest dataset, where MAE is significantly reduced by 13.39% and RMSE by 17.35% compared to the baseline (α=1). This work highlights the hybrid measure’s ability to address sparsity while improving prediction accuracy. The inclusion of similarities based on user tendencies toward popular items further enhances neighborhood selection, contributing to more accurate and personalized recommendations across diverse data distributions.

We present SUPER, a novel approach to mitigating popularity bias in recommender systems by explicitly modeling user-specific preferences for item popularity. Unlike existing methods that apply uniform adjustments to reduce the dominance of popular items, SUPER leverages each individual’s inclination toward mainstream versus niche content. By combining a Pareto-based item partitioning with personalized preference calibration, we incorporate both popular and tail subsets into the training and recommendation refinement processes. Experimental evaluations on multiple benchmark datasets demonstrate that SUPER substantially improves diversity and fairness while maintaining robust accuracy. Furthermore, our findings indicate that aligning the recommendations with each user’s interaction-derived popularity tendency promotes a broader exposure to underrepresented items and produces more satisfying recommendation experiences. These insights underscore the potential of personalized debiasing strategies to promote equitable and context-sensitive content curation in modern recommendation systems.

Ensuring equitable treatment of different user groups in recommender systems is a key challenge, and the issue of fairness has been widely explored in the literature. However, understanding fairness within a robust feedback loop, as it occurs in real-world settings, remains elusive. This study examines the interplay between popularity bias, calibration, accuracy, and beyond-accuracy performance of recommender systems using a novel dynamic feedback loop framework. The framework models iterative interactions between recommendation algorithms and user profiles, enabling the analysis of calibration, accuracy, and beyond-accuracy measures across user groups with varying preferences on popular items, i.e., Popular- , Diverse- , and Niche-focused . Empirical evaluations conducted on two benchmark datasets using three collaborative filtering algorithms reveal distinct disparities in how feedback loops affect different user groups. Niche-focused users, despite being the most active and information-rich, experience the steepest deterioration in system alignment over time, losing much of their initial calibration, long-tail exposure, and diversity advantages, along with proportional declines in accuracy. These results show that feedback dynamics progressively misalign the system with its most valuable users, making them the most disadvantaged over time. Popular-focused users remain most aligned with algorithmic tendencies, achieving steady accuracy gains but remaining confined to narrow, popularity-driven content with little to no long-tail exposure. Meanwhile, Diverse-focused users, initially balanced between popular and niche preferences, undergo gradual calibration drift and consistent reductions in diversity and long-tail representation, gradually converging toward recommendation patterns similar to Popular-focused users. Overall, the results demonstrate that feedback loops magnify structural inequalities, reinforcing popularity bias while reducing diversity and personalization across all user groups.

This study investigates the nonlinear optical properties of quantum wells (QWs) by analyzing the energy eigenvalues and eigenfunctions of confined electrons. The nonlinear optical rectification (NOR) coefficient was numerically calculated under various structural parameters and external fields. To establish relationships between the system’s energy eigenvalues, dipole moment matrix elements, and NOR coefficient, regression and correlation analyses were conducted using IBM SPSS Statistics. Linear, quadratic, and cubic regression models were evaluated, with the cubic model demonstrating the best fit. Model accuracy was assessed using several evaluation metrics, including coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) values for different external factors (temperature, pressure, electric field, barrier width, and barrier thickness). The results indicate that predictive modeling of the NOR coefficient enhances experimental efficiency by reducing cost and time while providing a reliable framework for understanding the optical behavior of QWs. This study offers a data-driven approach to optimizing nonlinear optical responses, contributing to advancements in optoelectronic applications.

This study examines how gender-based disparities emerge in recommender systems through feedback loops. While fairness has been studied in static settings, little is known about how repeated user-system interactions impact different demographic groups over time. To address this, we utilize a dynamic simulation framework using synthetic interactions and ten feedback iterations. Based on the MovieLens-1M dataset, users are grouped by gender and evaluated using metrics such as calibration, diversity, and long-tail exposure. Results show that female users consistently receive less favorable outcomes, with popularity bias measures (GAP, MRMC) indicating a growing disadvantage over time. Diversity and novelty scores also decline more sharply for women. These findings suggest that feedback loops can reinforce existing inequalities in recommender systems. The employed framework provides a valuable tool for analyzing the evolution of fairness across iterations and highlights the need for gender-sensitive algorithms that maintain fairness over time.

Popularity bias significantly limits the effectiveness of recommender systems by disproportionately favoring popular items and reducing exposure to diverse, less-known content. This bias negatively impacts personalization and marginalizes niche users and item providers. To address this challenge, calibrated recommendation methods have gained attention, notably the Calibrated Popularity (CP) approach, due to its simplicity, effectiveness, and model-agnostic nature. Originally, CP employs Jensen–Shannon divergence (JSD) to align the popularity distribution of recommended items with users’ historical interaction patterns. However, the choice of divergence measure substantially impacts calibration effectiveness and recommendation diversity. In this study, we systematically explore several alternative divergence measures, including Chi-Square, Wasserstein, Kullback–Leibler, Hellinger, Total Variation, Bhattacharyya, Cosine, and Renyi divergences, within the CP framework. Additionally, we propose a novel divergence-independent evaluation metric, namely Overall Similarity Error, enabling consistent assessment of calibration quality across divergence measures. Experimental results on two benchmark datasets using two collaborative filtering algorithms highlighted critical insights. More aggressive divergences, particularly Chi-Square, significantly enhanced calibration quality, reduced popularity bias, and increased recommendation diversity, albeit with a modest reduction in accuracy. In contrast, smoother divergences, such as JSD, maintained higher accuracy but provided limited improvements in reducing popularity bias. Also, the performed group-based analysis categorizing users into mainstream, balanced, and niche segments based on their historical popularity preferences revealed distinct patterns: balanced users typically achieved higher accuracy due to their evenly distributed preferences; mainstream users showed superior calibration results benefiting from robust signals of popular items; niche users obtained more diverse and personalized recommendations, clearly benefiting from aggressive divergence measures. These results underscore the complexity of addressing popularity bias and highlight the importance of adopting adaptive, user-aware calibration strategies to effectively balance accuracy, diversity, and fairness in recommender systems.

Recommender systems often suffer from popularity bias problem, favoring popular items and overshadowing less known or niche content, which limits recommendation diversity and content exposure. The root reason for this issue is the imbalances in the rating distribution; a few popular items receive a disproportionately large share of interactions, while the vast majority garner relatively few. In this study, we propose the EquiRate method as a pre-processing approach, addressing this problem by injecting synthetic ratings into less popular items to make the dataset regarding rating distribution more balanced. More specifically, this method utilizes several synthetic rating injection and synthetic rating generation strategies: (i) the first ones focus on determining which items to inject synthetic ratings into and calculating the total number of these ratings, while (ii) the second ones concentrate on computing the concrete values of the ratings to be included. We also introduce a holistic and highly efficient evaluation metric, i.e., the FusionIndex, concurrently measuring accuracy and several beyond-accuracy aspects of recommendations. The experiments realized on three benchmark datasets conclude that several EquiRate’s variants, with proper parameter-tuning, effectively reduce popularity bias and enhance recommendation diversity. We also observe that some prominent popularity-debiasing methods, when assessed using the FusionIndex, often fail to balance the referrals’ accuracy and beyond-accuracy factors. On the other hand, our best-performing EquiRate variants significantly outperform the existing methods regarding the FusionIndex, and their superiority is more apparent for the high-dimension data collections, which are more realistic for real-world scenarios.