OpenRaiser/Intern-Atlas huggingface dataset:
The 4th Workshop on Ethical Artificial Intelligence: Methods and Applications (EAI)
Authors: Chen Zhao, Feng Chen, Xintao Wu, Haifeng Chen
Venue: KDD
Year: 2025

As computers increasingly make decisions about who gets a loan, a job, or even bail, the expansion of AI algorithms has provoked public concern about ethical issues, and the need to understand what constitutes AI algorithms and how they make decisions becomes ever more pressing. For example, an increasing number of high-profile news reports that widely-used algorithms have unfairly discriminated against some groups of people (e.g., by gender and race) in parole decisions and other major life events. Focusing more attention on ethical bias in learning algorithms is key to unlocking the potential of automated decision systems while ensuring fairness and accountability so that everyone can advance equally in society. Ethical AI has become increasingly important and it has been attracting attention from academia and industry, due to its increased popularity in real-world applications with fairness concerns. It also places fundamental importance on ethical considerations in determining legitimate and illegitimate uses of AI. Organizations that apply ethical AI have clearly stated well-defined review processes to ensure adherence to legal guidelines. Therefore, the wave of research at the intersection of ethical AI in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science.
Verification of Incomplete Graph Unlearning through Adversarial Perturbations
Authors: Kun Wu, Wendy Hui Wang
Venue: KDD
Year: 2025

Graph unlearning (GU) enables data owners to remove specific data from a trained Graph Neural Network (GNN). However, a dishonest model provider may cheat on the unlearning process. This paper focuses on a specific type of cheating behavior in GU, namely incomplete edge unlearning, where the model provider removes only a subset of the requested edges from the trained GNN. We introduce PANDA, the first probabilistic GU verification framework, to detect such cheating behaviors. PANDA identifies a set of nodes, called token nodes, in the graph, and injects a set of fake edges, referred to as challenge edges, into the model to manipulate the predictions of token nodes. A key property of the challenge edges is that removing only a subset of them from the poisoned model does not change the token nodes' prediction. Then by requesting the removal of the challenge edges and observing the change in the token nodes' predictions, the verifier can assess the likelihood that the model provider has engaged in incomplete unlearning. To develop PANDA, we design a novel algorithm to identify the token nodes and generate their associated challenge edges. We rigorously quantify the verification probabilities achieved by PANDA. Our extensive empirical studies demonstrate the efficiency and effectiveness of PANDA in detecting incomplete edge unlearning across a variety of GNN models and unlearning algorithms. Furthermore, we show that PANDA exhibits strong robustness against state-of-the-art detection methods for graph adversarial perturbations. Our code and datasets are available at https://github.com/kunwu522/unlearning-verification-gnn.
Incomplete Multi-View Drug Recommendation via Multi-Level Representation Learning and Curriculum Learning
Authors: Ning Liu, Yunsen Tang, Haitao Yuan, Hongtao Lv, Lili Jiang, Zhen Li, Wei Zhang, Jianyong Wang
Venue: KDD
Year: 2025

The drug recommendation task aims to provide effective and safe prescription decision support for clinical treatment based on patients' past Electronic Health Records (EHR). However, the prevalent phenomenon of missing views in multi-source heterogeneous EHR data may cause performance degradation. This is due to the lack of sufficient information and increased learning difficulties, which limit the practical effectiveness of drug recommendation models in medical applications. In this paper, we emphasize the problems of incompleteness in practical drug recommendation and propose the Incomplete Multi-View Drug Recommendation model via Multi-Level Representation Learning and Curriculum Learning named IMDR. In particular, IMDR employs a Multi-Level Representation Learning architecture equipped with a Medical Code-Level Drug Knowledge Infusion Module and a Visit-Level Cross-View Information Module for patient representation learning to overcome the information loss caused by incomplete data. And then, a Gaussian-guided curriculum learning strategy is proposed to assist the learning process of IMDR with a novel difficulty measure to achieve effective progressive learning under missing medical views. Systematic evaluation on two large-scale real-world medical datasets, MIMIC-III and MIMIC-IV, demonstrates that IMDR reduces the Drug-Drug Interaction (DDI) rate by 2.97% compared to existing state-of-the-art drug recommendation baselines, while achieving significant improvements of 3.29% and 1.97% in Jaccard similarity scores and F1 score, respectively. Furthermore, compared to advanced incomplete multi-view learning (IML) models, IMDR's advantages in Jaccard similarity scores and F1 score further expand to 4.03% and 2.41%.
Loss or Gain: Hierarchical Conditional Information Bottleneck Approach for Incomplete Time Series Classification
Authors: Shuo Zhang, Jing Wang, Shiqin Nie, Jinghang Yue, Weikang Zhu, Youfang Lin
Venue: KDD
Year: 2025

Incomplete time series classification is both practically valuable and challenging as missing values in time series data are prevalent in real-world scenarios. Current approaches suffer from two major limitations. First, they overemphasize the consistency of data reconstruction during missing value imputation while neglecting the task-effectiveness of the imputed results for the classification. Second, they fail to systematically establish a synergistic optimization mechanism between data imputation and feature representation. To address these challenges, we propose a Hierarchical Conditional Information Bottleneck (HCIB) framework, which achieves incomplete time series classification through end-to-end joint optimization. Specifically, at the data imputation level, we re-examine the dual effects of missing data: the loss of critical information (Loss) versus the gain in interference suppression (Gain), elucidating this duality through bias-variance trade-off theory. Building on this analysis, we propose a task-information sufficiency criterion and extend the information bottleneck theory into a task-driven imputation framework by incorporating label information as a conditional constraint. At the feature representation level, we construct a hierarchical information bottleneck architecture to learn compressed yet informative temporal representations from the task-oriented imputed data. Furthermore, we derive the optimizable objective function for HCIB and design specialized neural network architectures for time series. Comprehensive experiments on multivariate and univariate time series datasets across multiple domains consistently demonstrate that the proposed method achieves significant improvements in classification performance compared to SOTA approaches.
SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis
Authors: Yu Liu, Weiyao Tao, Tong Xia, Simon Knight, Tingting Zhu
Venue: KDD
Year: 2025

Survival analysis, which estimates the probability of event occurrence over time from censored data, is fundamental in numerous real-world applications, particularly in high-stakes domains such as healthcare and risk assessment. Despite advances in numerous survival models, quantifying the uncertainty of predictions from these models remains underexplored and challenging. The lack of reliable uncertainty quantification limits the interpretability and trustworthiness of survival models, hindering their adoption in clinical decision-making and other sensitive applications. To bridge this gap, in this work, we introduce SurvUnc, a novel meta-model based framework for post-hoc uncertainty quantification for survival models. SurvUnc introduces an anchor-based learning strategy that integrates concordance knowledge into meta-model optimization, leveraging pairwise ranking performance to estimate uncertainty effectively. Notably, our framework is model-agnostic, ensuring compatibility with any survival model without requiring modifications to its architecture or access to its internal parameters. Especially, we design a comprehensive evaluation pipeline tailored to this critical yet overlooked problem. Through extensive experiments on four publicly available benchmarking datasets and five representative survival models, we demonstrate the superiority of SurvUnc across multiple evaluation scenarios, including selective prediction, misprediction detection, and out-of-domain detection. Our results highlight the effectiveness of SurvUnc in enhancing model interpretability and reliability, paving the way for more trustworthy survival predictions in real-world
Improving Long-tail User CTR Prediction via Hierarchical Distribution Alignment
Authors: Yifan Wang, Weizhi Ma, Min Zhang, Xiaoxiao Xu, Zhiqiang Liu, Shaoping Ma
Venue: KDD
Year: 2025

Click-Through Rate (CTR) prediction is a fundamental task in online advertising and recommender systems, requiring the effective modeling of feature interactions. While existing methods have improved overall prediction performance, the performance of long-tail users with limited historical data remains suboptimal. These users face two primary challenges: (i) insufficient training data leading to inaccurate predictions, and (ii) an imbalanced sample distribution that biases model learning toward head users. To address these challenges, we propose a novel framework that enhances long-tail user performance through hierarchical distribution alignment, hierarchical residual learning, and adaptive distrbution calibration. Our method first captures the shared patterns between head and long-tail users via hierarchical distribution alignment, then learns group-specific information through hierarchical residual learning. Additionally, we re-balance the sample distributions of head users and long-tail users by dynamic reweighting. To counteract potential biases introduced by reweighting, we further incorporate a distribution calibration module. Our method is model-agnostic and can be seamlessly integrated into various CTR prediction architectures that rely on feature interactions. Extensive experiments on public datasets and an online experiment demonstrate that our approach significantly improves accuracy and fairness for long-tail users while maintaining similar or even better overall performance.
Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning
Authors: Tong Li, Jiachuan Wang, Yongqi Zhang, Shuangyin Li, Lei Chen
Venue: KDD
Year: 2025

Citation classification, which identifies the intention behind academic citations, is pivotal for scholarly analysis. Previous works suggest fine-tuning pretrained language models (PLMs) on citation classification datasets, reaping the reward of the linguistic knowledge they gained during pretraining. However, directly fine-tuning for citation classification is challenging due to labeled data scarcity, contextual noise, and spurious keyphrase correlations. In this paper, we present a novel framework, Citss, that adapts the PLMs to overcome these challenges. Citss introduces self-supervised contrastive learning to alleviate data scarcity, and is equipped with two specialized strategies to obtain the contrastive pairs: sentence-level cropping, which enhances focus on target citations within long contexts, and keyphrase perturbation, which mitigates reliance on specific keyphrases. Compared with previous works that are only designed for encoder-based PLMs, Citss is carefully developed to be compatible with both encoder-based PLMs and decoder-based LLMs, to embrace the benefits of enlarged pretraining. Experiments with three benchmark datasets with both encoder-based PLMs and decoder-based LLMs demonstrate our superiority compared to the previous state of the art. Our code is available at: github.com/LITONG99/Citss
Beyond Locations: A Motion Range-Aware Similarity Join
Authors: Ke Li, Lisi Chen, Shuo Shang, Christian S. Jensen, Panos Kalnis
Venue: KDD
Year: 2025

With the proliferation of GPS-enabled devices such as smartphones, the querying of moving objects has attracted substantial attention, with studies covering joins, range and kNN queries, similarity queries, etc. Challenges arise due to variable sampling frequencies, potential inaccuracies in location samples, and the unavailability of locations between samples. Existing similarity joins often rely on discrete location samples, which fail to capture movement uncertainty and may miss meaningful interactions. To address this limitation, we propose Intersection Similarity Join (IS-Join), a novel approach that identifies object pairs based on the overlap of their motion ranges rather than location-based proximity. We define motion ranges as the spatial regions an object may traverse within a given time period, and introduce an intersection similarity measure that quantifies their overlap. To efficiently process IS-Join queries, we develop a Hybrid Ball-tree indexing structure with a repartitioning strategy, enabling scalable candidate filtering. Additionally, we introduce pre-checking and pruning techniques to further reduce computational overhead. Extensive experiments on two real-world trajectory datasets demonstrate that IS-Join significantly outperforms well-designed baselines, achieving up to a 3x reduction in runtime. Our work opens new opportunities for applications such as urban mobility analysis, traffic monitoring, wildlife tracking, and contact tracing.
THEMES: An Offline Apprenticeship Learning Framework for Evolving Reward Functions
Authors: Xi Yang, Md. Mirajul Islam, Ge Gao, Min Chi
Venue: KDD
Year: 2025

Apprenticeship learning (AL) aims to induce decision-making policies by observing and imitating expert demonstrations. Existing AL approaches typically rely on online interactions and assume that the demonstrations follow a single reward function. Nevertheless, in real-world human-centric applications, policies are usually learned in an offline setting, with the demonstrations driven by multiple reward functions that evolve over time. To address these challenges, we introduce a novel AL framework: Time-aware Hierarchical EM Energy-based Sub-trajectory THEMES clustering. We evaluate the effectiveness of THEMES in two challenging human-centric domains -- healthcare and education. Our experimental results across multiple datasets demonstrate that THEMES can accurately induce policies, outperforming competitive baselines and ablations, demonstrating its potential for tackling a broad range of complex, real-world human-centric tasks.
LettinGo: Explore User Profile Generation for Recommendation System
Authors: Lu Wang, Di Zhang, Fangkai Yang, Pu Zhao, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Qingwei Lin, Weiwei Deng, Dongmei Zhang, Feng Sun, Qi Zhang
Venue: KDD
Year: 2025

User profiling is pivotal for recommendation systems, as it transforms raw user interaction data into concise and structured representations that drive personalized recommendations. While traditional embedding-based profiles lack interpretability and adaptability, recent advances with large language models (LLMs) enable text-based profiles that are semantically richer and more transparent. However, existing methods often adhere to fixed formats that limit their ability to capture the full diversity of user behaviors. In this paper, we introduce LettinGo, a novel framework for generating diverse and adaptive user profiles. By leveraging the expressive power of LLMs and incorporating direct feedback from downstream recommendation tasks, our approach avoids the rigid constraints imposed by supervised fine-tuning (SFT). Instead, we employ Direct Preference Optimization (DPO) to align the profile generator with task-specific performance, ensuring that the profiles remain adaptive and effective. LettinGo operates in three stages: (1) exploring diverse user profiles via multiple LLMs(2) evaluating profile quality based on their impact in recommendation systems, and (3) aligning the profile generation through pairwise preference data derived from task performance. Experimental results demonstrate that our framework significantly enhances recommendation accuracy, flexibility, and contextual awareness. This work enhances profile generation as a key innovation for next-generation recommendation systems.
Hi-PNG: Efficient Interval-Filtering ANNS via Hierarchical Interval Partition Navigating Graph
Authors: Ming Yang, Yuzheng Cai, Weiguo Zheng
Venue: KDD
Year: 2025

Approximate nearest neighbor search (ANNS) is widely used to retrieve similar vectors from high-dimensional data. However, many real-world applications require additional interval filtering based on numerical constraints, such as stock price ranges. In this paper, we introduce interval-filtering ANNS (IF-ANNS), a novel yet general retrieval task where both base and query vectors are associated with numerical intervals. The goal is to retrieve nearest neighbors whose intervals are fully contained within the query interval. To efficiently address this problem, we propose Hierarchical Interval Partitioning Navigating Graph (Hi-PNG), a framework that hierarchically partitions the interval space to efficiently locate the search space, eliminating unnecessary computations and achieving significant speedups. We provide a theoretical analysis of the search space, demonstrating the superiority of our approach. Extensive experiments on eight datasets, including commonly used benchmarks and real-world stock price data, show that Hi-PNG outperforms four state-of-the-art graph-based ANNS baselines, achieving up to 15× acceleration while maintaining high precision. These results highlight Hi-PNG's effectiveness in solving the IF-ANNS problem. All codes and data generation are available at https://github.com/PUITAR/Hi-PNG.git.
ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis
Authors: Jian Chen, Peilin Zhou, Yining Hua, Dading Chong, Meng Cao, Yaowei Li, Wei Chen, Bing Zhu, Junwei Liang, Zixuan Yuan
Venue: KDD
Year: 2025

Meteorological heatmaps play a vital role in deciphering extreme weather phenomena, yet their inherent complexities-marked by irregular contours, unstructured patterns, and complex color variations-present unique analytical hurdles for state-of-the-art Vision-Language Models (VLMs). Current state-of-the-art models like GPT-4o, Qwen-VL, and LLaVA 1.6 struggle with tasks such as precise color identification and spatial localization, resulting in inaccurate or incomplete interpretations. To address these challenges, we introduce Sparse Position and Outline Tracking (SPOT), a novel algorithm specifically designed to process irregularly shaped colored regions in visual data. SPOT identifies and localizes these regions by extracting their spatial coordinates, enabling structured representations of irregular shapes. Building on SPOT, we construct ClimateIQA, a novel meteorological visual question answering (VQA) dataset, comprising 26,280 high-resolution heatmaps and 762,120 instruction samples for wind gust, total precipitation, wind chill index and heat index analysis. ClimateIQA enhances VLM training by incorporating spatial cues, geographic metadata, and reanalysis data, improving model accuracy in interpreting and describing extreme weather features. Furthermore, we develop Climate-Zoo, a suite of fine-tuned VLMs based on SPOT-empowered ClimateIQA, which significantly outperforms existing models in meteorological heatmap tasks.
UniERF: A Uniform Embedding-based Retrieval Framework for E-commerce Search
Authors: Hao Jiang, Xiaoyu He, Fanyi Qu, Congcong Liu, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao
Venue: KDD
Year: 2025

E-commerce has become an integral part of daily life, and the ability to effectively retrieve items relevant to a user's query is crucial for enhancing the shopping experience. Embedding-based retrieval (EBR) has proven to be an effective approach in industrial e-commerce search systems. This method involves training models to generate high-quality representations of queries and items, followed by the use of efficient approximate nearest neighbor (ANN) search techniques to find relevant items. However, current EBR methods face several critical limitations: (1) multiple EBR branches often retrieve overlapping or redundant result sets; (2) allocation and utilization of computational resources remain suboptimal, hindering performance; (3) the differentiation modeling of features is somewhat neglected, which restricts the system's ability to retrieve diverse and representative results. These issues hinder the retrieval performance of online search systems. In this paper, we introduce a novel e-commerce search framework called the Uniform Embedding-based Retrieval Framework (UniERF). This framework is meticulously designed to incorporate diverse samples for joint model training, enabling the model to effectively leverage both the semantic information of queries and the personalized features of different users. Extensive offline and online experiments demonstrate that UniERF surpasses baseline methods across various evaluation metrics. UniERF has been successfully implemented in the existing retrieval system at JD.COM, a renowned online shopping website.
CAMEF: Causal-Augmented Multi-Modality Event-Driven Financial Forecasting by Integrating Time Series Patterns and Salient Macroeconomic Announcements
Authors: Yang Zhang, Wenbo Yang, Jun Wang, Qiang Ma, Jie Xiong
Venue: KDD
Year: 2025

Accurately forecasting the impact of macroeconomic events is critical for investors and policymakers. Salient events like monetary policy decisions and employment reports often trigger market movements by shaping expectations of economic growth and risk, thereby establishing causal relationships between events and market behavior. Existing forecasting methods typically focus either on textual analysis or time-series modeling, but fail to capture the multi-modal nature of financial markets and the causal relationship between events and price movements. To address these gaps, we propose CAMEF (Causal-Augmented Multi-Modality Event-Driven Financial Forecasting), a multi-modality framework that effectively integrates textual and time-series data with a causal learning mechanism and an LLM-based counterfactual event augmentation technique for causal-enhanced financial forecasting. Our contributions include: (1) a multi-modal framework that captures causal relationships between policy texts and historical price data; (2) a new financial dataset with six types of macroeconomic releases from 2008 to April 2024, and high-frequency real trading data for five key U.S. financial assets; and (3) an LLM-based counterfactual event augmentation strategy. We compare CAMEF to state-of-the-art transformer-based time-series and multi-modal baselines, and perform ablation studies to validate the effectiveness of the causal learning mechanism and event types.