The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions.Most existing approaches frame the task as sequential prediction based on users' historical purchase records.Although effective in capturing users' natural preferences, this formulation falls short in accurately modeling actual recommendation scenarios, particularly in accounting for how unsuccessful recommendations influence future purchases.Furthermore, the impact of the RecSys itself on users' decisions has not been appropriately isolated and quantitatively analyzed.To address these challenges, we propose a novel formulation of sequential recommendation, called Causal Sequential Recommendation.Instead of merely predicting the next item in a sequence, CSRec distinguishes between a user's natural preference and their actual purchasing decision.It predicts both aspects within a sequential context and traces how current decisions are formed and causally influenced by various factors.Applying such a causal framework can isolate the impact of recommender systems on user decisions, thereby opening new avenues for evaluation and design.This includes assessing how different strategies influence users' trust in the system and determining the optimal recommender system to maximize advertising benefits.CSRec can be seamlessly integrated into existing next-prediction-based methodologies.Experimental evaluations on both synthetic and real-world datasets demonstrate that the proposed implementation significantly improves upon state-of-the-art baselines.[code can be accessed here].

Fair decision-making in Machine Learning (ML) remains a critical challenge, particularly when access to sensitive information is restricted due to legal, ethical, or organizational constraints. These limitations affect both accuracy and fairness, creating tradeoffs central to the deployment of ML systems in the real world. While prior work has studied fairness-accuracy tradeoffs, most approaches focus on model outputs rather than directly examining how restricted data access impacts fairness. This leaves an important gap: understanding how fairness constraints affect model performance under real-world data restrictions . To address this gap, we propose a framework that explicitly models fairness-accuracy tradeoffs in data-restricted environments. Unlike prior work, our approach analyzes the behavior of the optimal Bayesian classifier using a discrete approximation of the data distribution, allowing us to systematically isolate the effects of fairness constraints. We evaluate our framework on three benchmark datasets—Adult, Law, and Dutch Census—revealing key insights: (1) enforcing equal accuracy on imbalanced datasets can substantially degrade performance under additional fairness constraints, (2) individual and group fairness often impose conflicting constraints, and (3) decorrelating sensitive attributes from features does not usually reduce accuracy. These findings demonstrate that our framework provides an effective, structured approach for practitioners to assess fairness constraints in decision-making pipelines.

Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention. While many existing studies improve fairness at the group level, only a few works promote individual fairness, which renders similar outcomes for similar individuals. A desirable framework that promotes individual fairness should (1) balance fairness and performance, (2) accommodate two commonly-used individual similarity measures (externally annotated and computed from input features), and, (3) generalize across various GNNs. Unfortunately, none of the prior work achieves all the desirables. In this work, we propose a novel method, GFairHint , which promotes individual fairness in GNNs and achieves all aforementioned desirables. GFairHint learns fairness representations through an auxiliary link prediction task, which is inspired by a theoretical analysis of the definition of individual fairness. We then concatenate the representations with the learned node embeddings in original GNNs as a “fairness hint” . Through extensive experimental investigations on five real-world graph datasets under three prevalent GNNs covering both individual similarity measures above, GFairHint achieves the best fairness results in almost all combinations of datasets with various backbone models, while generating comparable utility results, with much less computational cost compared to the previous state-of-the-art method.

Scalable probabilistic modeling and prediction in high dimensional multivariate time-series, such as dynamic social networks with co-evolving nodes and edges, is a challenging problem, particularly for systems with hidden sources of dependence and/or homogeneity. Here, we address this problem through the discovery of hierarchical latent groups. We introduce a family of Conditional Latent Tree Models (CLTM), in which tree-structured latent variables incorporate the unknown groups. The latent tree itself is conditioned on observed covariates such as seasonality, historical activity, and node attributes. We propose a statistically efficient framework for learning both the hierarchical tree structure and the parameters of the CLTM. We demonstrate competitive performance on two real world datasets, one from the students' attempts at answering questions in a psychology MOOC and the other from Twitter users participating in an emergency management discussion and interacting with one another. In addition, our modeling framework provides valuable and interpretable information about the hidden group structures and their effect on the evolution of the time series.