Mykola Pechenizkiy

Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the over-representation of high rating values. Although each bias has been studied independently, their combined effect , to which we refer to as multifactorial bias , remains underexplored. In this work, we examine how multifactorial bias influences item-side fairness, focusing on exposure bias, which reflects the unequal visibility of items in recommendation outputs. Through simulation studies, we find that positivity bias is disproportionately concentrated on popular items, further amplifying their over-exposure. Motivated by this insight, we adapt a percentile-based rating transformation as a pre-processing strategy to mitigate multifactorial bias. Experiments using six recommendation algorithms across four public datasets show that this approach improves exposure fairness with negligible accuracy loss. We also demonstrate that integrating this pre-processing step into post-processing fairness pipelines enhances their effectiveness and efficiency, enabling comparable or better fairness with reduced computational cost. These findings highlight the importance of addressing multifactorial bias and demonstrate the practical value of simple, data-driven pre-processing methods for improving fairness in recommender systems.

Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) aims to aggregate knowledge from multiple source models and transfer it to a target domain without accessing source data. Extending MSFUDA to graph-structured data (MSFUGDA) is particularly challenging due to the non-i.i.d. nature of graphs. Challenges remain largely underexplored with key issues arising from suboptimal coarse-grained source model aggregation and limited utilization of graph topology in unsupervised learning. To tackle these challenges, we propose Evidential Graph Domain Adaptation (EviGDA) network, an evidential learning–based framework for MSFUGDA. EviGDA explicitly quantifies the uncertainty of each target sample relative to different source models, which provides a principled characterization of target-specific model preferences. Leveraging this uncertainty-aware formulation, we develop an evidential-based multi-source aggregation mechanism that enables fine-grained, instance-level aggregation of source knowledge. Furthermore, to account for the distinctive properties of graph-structured data, EviGDA derives pseudo-labels for target samples from both the feature space and graph topology, thereby enabling an effective unsupervised learning objective that guides model optimization. To the best of our knowledge, this work is the first to introduce evidential theory into MSFUGDA, establishing a fundamentally new paradigm distinct from existing approaches. Extensive experiments demonstrate that EviGDA achieves superior performance in comparison with state-of-the-art methods.

In high-dimensional data, features often exhibit complex correlations and redundancies that hinder effective learning and reduce model interpretability. Therefore, extracting critical feature structural information from such data is essential for effective feature selection. This structural information can be represented as a feature graph, which reveals associations among features, including redundancies and correlations. However, most existing feature graph-based methods adopt a single strategy, focusing either on selecting relevant features or removing redundant ones, often resulting in suboptimal performance. Moreover, these methods typically construct static feature graphs using all input features, which may introduce irrelevant or detrimental relationships. Such simplistic approaches and low-quality graphs limit the effectiveness of feature selection. To address these limitations, we propose the Dynamic Feature Graph (DFG) framework that jointly learns feature graphs and selects features. By decoupling feature associations according to category, the DFG framework dynamically identifies key feature relationships, eliminating redundancy across classes while preserving essential intra-class correlations. This results in the selection of both relevant and non-redundant features. Additionally, we introduce sample manifolds with a rank equality constraint to ensure the selection of category-related features. To the best of our knowledge, this is the first dynamic feature graph approach in the field. Extensive experiments on 15 widely used real-world datasets demonstrate that DFG outperforms 20 state-of-the-art feature selection methods, highlighting its effectiveness in supervised and unsupervised settings.

Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the over-representation of high rating values. Although each bias has been studied independently, their combined effect, to which we refer to as multifactorial bias, remains underexplored. In this work, we examine how multifactorial bias influences item-side fairness, focusing on exposure bias, which reflects the unequal visibility of items in recommendation outputs. Through simulation studies, we find that positivity bias is disproportionately concentrated on popular items, further amplifying their over-exposure. Motivated by this insight, we adapt a percentile-based rating transformation as a pre-processing strategy to mitigate multifactorial bias. Experiments using six recommendation algorithms across four public datasets show that this approach improves exposure fairness with negligible accuracy loss. We also demonstrate that integrating this pre-processing step into post-processing fairness pipelines enhances their effectiveness and efficiency, enabling comparable or better fairness with reduced computational cost. These findings highlight the importance of addressing multifactorial bias and demonstrate the practical value of simple, data-driven pre-processing methods for improving fairness in recommender systems.

Fairness-aware machine learning (fair-ml) techniques are algorithmic interventions designed to ensure that individuals who are affected by the predictions of a machine learning model are treated fairly. The problem is often posed as an optimization problem, where the objective is to achieve high predictive performance under a quantitative fairness constraint. However, any attempt to design a fair-ml algorithm must assume a world where Goodhart’s law has an exception: when a fairness measure becomes an optimization constraint, it does not cease to be a good measure. In this article, we argue that fairness measures are particularly sensitive to Goodhart’s law. Our main contributions are as follows. First, we present a framework for moral reasoning about the justification of fairness metrics. In contrast to existing work, our framework incorporates the belief that whether a distribution of outcomes is fair depends not only on the cause of inequalities but also on what moral claims decision subjects have to receive a particular benefit or avoid a burden. We use the framework to distill moral and empirical assumptions under which particular fairness metrics correspond to a fair distribution of outcomes. Second, we explore the extent to which employing fairness metrics as a constraint in a fair-ml algorithm is morally justifiable, exemplified by the fair-ml algorithm introduced by Hardt et al. [ 21 ]. We illustrate that enforcing a fairness metric through a fair-ml algorithm often does not result in the fair distribution of outcomes that motivated its use and can even harm the individuals the intervention was intended to protect.