Kaike Zhang

Real-world user behaviors are often noisy due to factors such as human errors, uncertainty, and behavioral ambiguity, which can lead to degraded recommendation performance. To address this issue, recent approaches widely adopt self-supervised learning (SSL), particularly contrastive learning, by generating perturbed views of user interaction sequences and maximizing their mutual information to improve model robustness. However, these methods heavily rely on their pre-defined static augmentation strategies~(where the augmentation type remains fixed once chosen) to construct augmented views, leading to two critical challenges: (1) the optimal augmentation type can vary significantly across different scenarios; (2) inappropriate augmentations may even degrade recommendation performance, limiting the effectiveness of SSL. To overcome these limitations, we propose an adaptive augmentation framework. We first unify existing basic augmentation operations into a unified formulation via structured transformation matrices. Building on this formulation, we introduce AsarRec, an Adaptive Sequential Augmentation for Robust Sequential Recommendation. To enable stable end-to-end optimization of discrete and strongly constrained augmentations, AsarRec learns to generate transformation matrices by encoding user sequences into probabilistic transition matrices and projecting them into hard semi-doubly stochastic matrices via a differentiable Semi-Sinkhorn algorithm. To ensure that the learned augmentations benefit downstream performance, we jointly optimize three objectives: diversity (encouraging distinct views), semantic invariance (preserving semantic consistency among views), and informativeness (identifying augmentations most beneficial to recommendation). Extensive experiments on four benchmarks under varying noise levels validate the effectiveness of AsarRec, demonstrating its superior robustness and consistent improvements.

Artificial intelligence (AI) systems are playing an increasingly crucial role in people's lives, and their frequent unfair behaviors raise concerns about fairness. To unveil the unfairness in AI systems, researchers conduct fairness auditing on these systems. However, existing fairness auditing works often focus on group fairness while ignoring discriminatory phenomena among individuals. To unearth discriminatory phenomena against individuals within AI systems, this paper proposes an individual fairness auditing framework, termed ''substantiating'', which can identify discrimination instances within AI systems by constructing individual samples. To construct these samples for substantiating, auditors often have to rely on subjective prior knowledge, lacking guidelines on how to construct unfair samples. To address this issue, this paper introduces two categories of automated sample generation methods that can rapidly find unfair samples within a limited number of queries to the system and demonstrate their effectiveness through experiments. This paper evaluates the proposed auditing framework among three categories of stakeholders in AI fairness: auditors, AI model developers, and non-technical personnel. The research findings point out their demand for individual fairness audits of AI systems and highlight how the framework supports a reliable and convenient individual fairness audit.

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters “dirty” data, where noise or malicious information can lead to abnormal recommendations. Research on improving robustness of recommender systems against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on robust recommender systems. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks, and regularization, purification, self-supervised learning for defending against malicious attacks. Additionally, we summarize evaluation metrics and commonly used datasets for assessing robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to provide readers with a comprehensive understanding of robust recommender systems and to identify key pathways for future research and development. To facilitate ongoing exploration, we maintain a continuously updated GitHub repository with related research: https://github.com/Kaike-Zhang/Robust-Recommender-System .