Large Language Models (LLMs) are now widely deployed across modern web services, but their safe and trustworthy use in real-world settings critically depends on accurate alignment with human preferences. Preference alignment is typically achieved using methods such as reinforcement learning or direct preference optimization (DPO), whose effectiveness in practice hinges on the quality of labeled preference data. However, a fundamental practical challenge remains: preference datasets inevitably contain noise. Through a systematic analysis of mainstream preference datasets, we find that roughly 25% of preference pairs show clear inconsistencies between reward-model evaluations and human annotations. Such inconsistent examples do not convey reliable preference signals; training directly on them therefore not only fails to improve alignment but can even degrade model behavior.

Sequential recommendation aims to learn users' dynamic preferences from their historical interactions and predict the next item they are most likely to engage with. In real-world scenarios, time-varying factors (e.g., product promotions, seasonal changes) induce distribution shifts in user interactions. Despite the demonstrated success of existing models, their generalization capability remains limited under such dynamic conditions. Current methods tackle this challenge by leveraging distributionally robust optimization (DRO) to optimize the "worst-case" loss or by employing manually designed data augmentation to enrich the training distribution. Despite their effectiveness, DRO-based approaches are inherently constrained by the sparsity of training data, limiting the range of distributions they can model, while manually designed augmentations risk introducing noise or irrelevant information that could distort user preference learning. Furthermore, these methods often overlook the sensitivity of user interactions to distribution shifts, which is essential for capturing the stable factors in the evolution of user preferences in real-world settings.

Personalized learner modeling uses learners' historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD).This is essential for web-based learning services such as learning resource recommendation and adaptive testing.However, prior studies have shown that CD models may unfairly correlate learners' abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes.While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical.Furthermore, the presence of multiple sensitive attributes complicates fairness improvements.In this paper, we explore how to achieve fair personalized learner modeling without * Min Hou is the corresponding author.

Local spatial-aware community detection refers to detecting a spatial-aware community for a given node using local information. A spatial-aware community means that nodes in the community are tightly connected in structure, and their locations are close to each other. Existing studies focus on detecting the local non-overlapping spatial-aware community, i.e., detecting a spatial-aware community containing the given node. However, many geosocial networks often contain overlapping spatial-aware communities. Therefore, we propose a local overlapping spatial-aware community detection (LOSCD) problem, which aims to detect all spatial-aware communities that contain a given node with local information. To address LOSCD problem, we design an algorithm based on Spatial Modularity and Edge Similarity, called SMES. SMES contains two processes: spatial expansion and structure detection. The spatial expansion process involves using spatial modularity to identify nodes that are spatially close, while the structural detection process employs edge similarity to identify nodes that are structurally close. Experimental results demonstrate that SMES outperforms comparison algorithms in terms of both structural and spatial cohesiveness.