OpenRaiser/Intern-Atlas huggingface dataset:
WDformer: A Wavelet-based Differential Transformer Model for Time Series Forecasting
Authors: Xiaojian Wang, Chaoli Zhang, Zhonglong Zheng, Yunliang Jiang
Venue: CIKM
Year: 2025

Time series forecasting has various applications, such as meteorological rainfall prediction, traffic flow analysis, financial forecasting, and operational load monitoring for various systems. Due to the sparsity of time series data, relying solely on time-domain or frequency-domain modeling limits the model's ability to fully leverage multi-domain information. Moreover, when applied to time series forecasting tasks, traditional attention mechanisms tend to over-focus on irrelevant historical information, which may introduce noise into the prediction process, leading to biased results. We proposed WDformer, a wavelet-based differential Transformer model. This study employs the wavelet transform to conduct a multi-resolution analysis of time series data. By leveraging the advantages of joint representation in the time-frequency domain, it accurately extracts the key information components that reflect the essential characteristics of the data. Furthermore, we apply attention mechanisms on inverted dimensions, allowing the attention mechanism to capture relationships between multiple variables. When performing attention calculations, we introduced the differential attention mechanism, which computes the attention score by taking the difference between two separate softmax attention matrices. This approach enables the model to focus more on important information and reduce noise. WDformer has achieved state-of-the-art (SOTA) results on multiple challenging real-world datasets, demonstrating its accuracy and effectiveness. Code is available at https://github.com/xiaowangbc/WDformer.
Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Authors: Xinye Wanyan, Danula Hettiachchi, Chenglong Ma, Ziqi Xu, Jeffrey Chan
Venue: CIKM
Year: 2025

Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation, driving interest in using LLM-based agents to simulate human feedback in recommender systems. However, most existing approaches rely on static user profiling, neglecting the temporal and dynamic nature of user interests. This limitation stems from a disconnect between language modelling and behaviour modelling, which constrains the capacity of agents to represent sequential patterns. To address this challenge, we propose a Dynamic Temporal-aware Agent-based simulator for Recommender Systems, DyTA4Rec, which enables agents to model and utilise evolving user behaviour based on historical interactions. DyTA4Rec features a dynamic updater for real-time profile refinement, temporal-enhanced prompting for sequential context, and self-adaptive aggregation for coherent feedback. Experimental results at group and individual levels show that DyTA4Rec significantly improves the alignment between simulated and actual user behaviour by modelling dynamic characteristics and enhancing temporal awareness in LLM-based agents.
Exploration and Visualization of a Legal Knowledge Graph: A Human-Centered Approach
Authors: Sabine Wehnert, Pramod Kumar Bontha, Kilian Lüders, Huu Huong Giang Nguyen, Ernesto William De Luca
Venue: CIKM
Year: 2025

Building applications for users in the legal domain is challenging due to their strict requirements. In this work, we present a prototype combining search in legal norms, legal cases, and legal textbooks with traversal options for the references between these documents, modeled in a knowledge graph in the backend. We conducted a usability test with law students as one of the main target user groups to evaluate the prototype. The usability test comprised user surveys, retrieval test tasks, and a law exam. In our study (n = 20), prototype users solved significantly more retrieval tasks than controls (M = 12.7 vs. 7.5, p = .006) and rated usability at 62.3 SUS points, identifying clear advantages in citation traversal and textbook reference tasks. While exam scores showed no statistically significant difference, qualitative feedback confirmed improved efficiency and satisfaction. We also compare user performance to Large Language Models (LLMs) in vanilla and Retrieval-Augmented Generation configurations, motivated by participant interest in AI-assisted features.
CAGCL: A Community-Aware Graph Contrastive Learning Model for Social Bot Detection
Authors: Kaihang Wei, Min Teng, Haotong Du, Songxin Wang, Jinhe Zhao, Chao Gao
Venue: CIKM
Year: 2025

Malicious social bot detection is vital for social network security. While graph neural networks (GNNs) based methods have improved performance by modeling structural information, they often overlook latent community structures, resulting in homogeneous node representations. Leveraging community structures, which capture discriminative group-level patterns, is therefore essential for more robust detection. In this paper, we propose a new Community-Aware Graph Contrastive Learning (CAGCL) framework for enhanced social bot detection. Specifically, CAGCL first exploits the latent community structures to uncover the potential group-level patterns. Then, a dual-perspective community enhancement module is proposed, which strengthens the structural awareness and reinforces topological consistency within communities, thereby enabling more distinctive node representations and deeper intra-community message passing. Finally, a community-aware contrastive learning module is proposed, which considers nodes within the same community as positive pairs and those from different communities as negative pairs, enhancing the discriminability of node representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that CAGCL consistently outperforms state-of-the-art baselines. The code is available at https://github.com/cgao-comp/.
DANet: A RAG-inspired Dual Attention Model for Few-shot Time Series Prediction
Authors: Zimo Wen, Hanwen Hu, Nan Fang, Shiyou Qian, Jian Cao
Venue: CIKM
Year: 2025

Practical applications often require forecasting the future states of short time series (STS) using multiple related long time series (LTS) as auxiliary data, a process known as few-shot prediction. The primary challenge, given the limited data on STS, is effectively capturing the pattern similarities between STS and LTS. Current methods, despite notable advancements, primarily focus on trans- ferring pattern characteristics from LTS to STS without explicitly addressing their similarities at various levels. To overcome this lim- itation, we propose a novel few-shot time series forecasting model called DANet. Drawing on the Retrieval-Augmented Generation (RAG) framework in large language models, DANet retrieves long and short sequences from LTS that closely resemble STS, thereby enhancing prediction accuracy while simultaneously reducing un- certainty through this retrieval process. First, we define two metrics to quantify pattern similarities between STS and LTS, addressing the issue of different representations of the same pattern due to variations in sequence length. Second, we propose a dual-attention mechanism which embeds the two similarities metrics to extract and integrate long and short sequences from LTS across variable and temporal levels for generating predictions. Our experiments across six scenarios show that DANet significantly outperforms six state-of-the-art (SOTA) methods.
FedFMD: Fairness-Driven Adaptive Aggregation in Federated Learning via Mahalanobis Distance
Authors: Xiuting Weng, Lixing Yu, Shaojie Zhan, Ruizhi Pu, Xiaofei Liu
Venue: CIKM
Year: 2025

Federated learning (FL) facilitates collaborative global model training without compromising data privacy. However, data distribution variations among clients inevitably introduce bias in global updates, impacting model fairness and performance. Existing methods assign client aggregation weights simply based on dataset size proportions or rely on substantial assumptions about specific global data distributions such as uniform label distributions. These approaches inadequately capture the intrinsic impact of Non-IID data characteristics on model divergence. To address these deficiencies, we propose a novel adaptive weight allocation algorithm, FedFMD, leveraging Mahalanobis distance, integrating Task Arithmetic, to dynamically assign weights based on client contributions. FedFMD explicitly models task-centric deviations caused by data heterogeneity without requiring raw data access or prior distribution assumptions. Besides, FedFMD enhances aggregation weights computation through time-decay adjustments, guided by historical client performance trends, optimizing both fairness and utility. Extensive evaluations against six state-of-the-art (SOTA) algorithms and two distance metrics across three datasets demonstrate the superior performance of FedFMD in fairness and utility.
CHEM: Causally and Hierarchically Explaining Molecules
Authors: Gyeongdong Woo, Soyoung Cho, Donghyeon Kim, Kimoon Na, Changhyun Kim, Jinhee Choi, Jong-June Jeon
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) have significantly advanced in analyzing graph-structured data; however, their explainability remains challenging, affecting their applicability in critical domains such as medicine and pharmacology. In particular, violating the subgraph structure can degrade model interpretability and generalization performance. To address this problem, we propose a hierarchical and explainable causal inference-based GNN. Our model selects features based on explainable subgraph units informed by prior knowledge. Our method begins by clustering molecules into functional groups via the BRICS algorithm, then constructing a hierarchical structure at both the node and motif levels. The proposed model employs a gate module that distills causal features on the motif level and a loss function that disconnects information flow from non-causal features to the target level. The classification results on real-world molecular graphs demonstrate that our model outperforms other causal inference-based GNN models. In addition, it is confirmed that leveraging molecular docking data effectively identifies true causal substructures in the proposed model.
Empowering Denoising Sequential Recommendation with Large Language Model Embeddings
Authors: Tongzhou Wu, Yuhao Wang, Maolin Wang, Chi Zhang, Xiangyu Zhao
Venue: CIKM
Year: 2025

Sequential recommendation aims to capture user preferences by modeling sequential patterns in user-item interactions. However, these models are often influenced by noise such as accidental interactions, leading to suboptimal performance. Therefore, to reduce the effect of noise, some works propose explicitly identifying and removing noisy items. However, we find that simply relying on collaborative information may result in an over-denoising problem, especially for cold items. To overcome these limitations, we propose a novel framework: Interest Alignment for Denoising Sequential Recommendation (IADSR) which integrates both collaborative and semantic information. Specifically, IADSR is comprised of two stages: in the first stage, we obtain the collaborative and semantic embeddings of each item from a traditional sequential recommendation model and an LLM, respectively. In the second stage, we align the collaborative and semantic embeddings and then identify noise in the interaction sequence based on long-term and short-term interests captured in the collaborative and semantic modalities. Our extensive experiments on four public datasets validate the effectiveness of the proposed framework and its compatibility with different sequential recommendation systems. The code and data are released for reproducibility: https://github.com/Applied-Machine-Learning-Lab/IADSR.
Multi-Resource-Aware Admission Control for Online Data Processing
Authors: Ruoyu Wu, Wei Bao, Hequn Wang
Venue: CIKM
Year: 2025

Online data processing platforms offer free-tier services to attract users, where the service provider must strategically utilize limited resources to handle large amounts of low-value requests. Admission control has been leveraged to choose requests to process or skip, yet request uncertainties like unknown rewards and request numbers, and complicated interdependence among multi-resource consumption further pose challenges. To tackle these challenges, we propose a novel admission control solution, the Online Multi-resource Magician's Admission (OMMA) algorithm, that balances resource consumption with reward accumulation across online requests, while coordinating the intertwined consumption of different resources. OMMA is an online algorithm, with its performance evaluated by competitive ratio. OMMA attains a competitive ratio of C(1-K-1/2)M(1-L-1/2)N, where C is the constant capturing the intrinsic limitation of resource availability, K is the individual resource budget, M is the number of individual resources, L is the joint resource budget, and N is the number of joint resources. The competitive ratio achieved by OMMA is tight, meaning that it can be achieved by OMMA in certain problem instances. We implement trace-driven experiments to evaluate the practical performance of OMMA on a real-world LLM prompt dataset, demonstrating the superior performance of OMMA in online data processing services.
Asking Questions with Thoughts: An Efficient Difficulty-Controllable Question Generation Method with Posterior Knowledge Distillation
Authors: Sixing Wu, Jiahao Chen, Yujue Zhou, Zhijun Yang, Wei Zhou
Venue: CIKM
Year: 2025

Difficulty Controllable Question Generation (DCQG) for reading comprehension learns to generate questions for measuring the reading abilities of examinees, playing a crucial role in educational scenarios. This work studies answer-aware DCQG, a challenging task that requires the generated questions to remain faithful to the assigned answer and match the desired difficulty level at the same time. To this end, we first propose an effective two-stage framework, Asking Questions with Thoughts (AQT), to guide a backbone large language model (LLM) to generate questions that are both faithful and difficulty-aware through conducting in-depth self-thinking. Then, we introduce a novel Posterior Knowledge Distillation (PKD) to efficiently fine-tune AQT by distilling knowledge from posterior inference. Finally, to address the scarcity of DCQG datasets, we use an efficient LLM Pretest-based Difficulty Estimation (LP-DE) to automatically construct DCQG datasets from common QG/QA datasets. Extensive experiments prove that our methods have promising results in terms of both faithfulness and difficulty awareness.
Adaptive Context-Infused Performance Evaluator for Iterative Feature Space Optimization
Authors: Yanping Wu, Yanyong Huang, Zijun Yao, Yanjie Fu, Kunpeng Liu, Xiao Luo, Dongjie Wang
Venue: CIKM
Year: 2025

Iterative feature space optimization includes continuously evaluating and refining the feature space to improve downstream task performance. However, existing methods commonly suffer from three major limitations: 1) ignoring differences between samples leads to evaluation bias; 2) the feature space is overly tailored to specific models, resulting in overfitting and poor generalization; and 3) retraining the evaluator from scratch in each iteration significantly reduces overall efficiency. To bridge these gaps, we introduce EASE (gEneralized Adaptive feature Space Evaluator), a generalized framework for efficient and objective evaluation of iteratively generated feature spaces. This framework includes two key components: Feature-Sample Subspace Generator and Contextual Attention Evaluator. The first component aims to mitigate evaluation bias by decoupling the information distribution within the feature space. To achieve this, based on feedback from the subsequent evaluator, we identify the samples most challenging for evaluation and the features most relevant to prediction tasks. The second component intends to incrementally capture evolving patterns of the feature space for efficient evaluation. Specifically, we propose a weighted-sharing multi-head attention mechanism to encode the feature space into an embedding vector for evaluation, and update the evaluator incrementally to retain prior knowledge while incorporating new information. Extensive experiments on fifteen public datasets demonstrate the effectiveness of EASE. We have released our code and data to the public.
ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
Authors: Binqing Wu, Jianlong Huang, Zongjiang Shang, Ling Chen
Venue: CIKM
Year: 2025

In multivariate time series (MTS) forecasting, many deep learning based methods have been proposed for modeling dependencies at multiple spatial (inter-variate) or temporal (intra-variate) scales. However, existing methods may fail to model dependencies across multiple spatial-temporal scales (ST-scales, i.e., scales that jointly consider spatial and temporal scopes). In this work, we propose ST-Hyper to model the high-order dependencies across multiple ST-scales through adaptive hypergraph modeling. Specifically, we introduce a Spatial-Temporal Pyramid Modeling (STPM) module to extract features at multiple ST-scales. Furthermore, we introduce an Adaptive Hypergraph Modeling (AHM) module that learns a sparse hypergraph to capture robust high-order dependencies among features. In addition, we interact with these features through tri-phase hypergraph propagation, which can comprehensively capture multi-scale spatial-temporal dynamics. Experimental results on six real-world MTS datasets demonstrate that ST-Hyper achieves the state-of-the-art performance, outperforming the best baselines with an average MAE reduction of 3.8% and 6.8% for long-term and short-term forecasting, respectively. Code is available at https://anonymous.4open.science/ST-Hyper-83E7.
Spatio-Temporal Forecasting under Open-World Missingness with Adaptive Mixture-of-Experts
Authors: Chenyu Wu, Zhipeng Ma, Junbo Zhang, Songyu Ke, Yu Zheng
Venue: CIKM
Year: 2025

Spatio-temporal forecasting is crucial for sustainable urban development and societal decision-making. However, real-world spatio-temporal data often exhibit open-world missingness: missing rates and patterns evolve dynamically across time and space, severely disrupting dependencies and challenging accurate forecasting. Traditional methods universally overlook the dynamic nature of missingness, resulting in degraded predictive accuracy. To address this gap, we propose a novel Spatio-Temporal Missing-aware Mixture-of-Experts (STMMoE) architecture, equipped with a three-stage training strategy. STMMoE dynamically adapts to varying missing rates through a gating mechanism that selects specialized expert branches. The three-stage training strategy improves end-to-end forecasting performance by aligning the representations of complete and missing data. Extensive experiments on two real-world datasets show that our method achieves state-of-the-art performance.
STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning
Authors: Chenghao Wu, Ruiyang Ren, Junjie Zhang, Ruirui Wang, Zhongrui Ma, Qi Ye, Wayne Xin Zhao
Venue: CIKM
Year: 2025

While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities. Each user is modeled as an agent with parallel cognitions: fast response for immediate interactions and slow reasoning that performs chain-of-thought rationales. To cultivate intrinsic slow thinking, we develop anchored reinforcement training-a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping. This hybrid approach scaffolds agents in acquiring foundational capabilities (preference summarization, rationale generation) while enabling dynamic policy adaptation through simulated feedback loops. Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines, despite using only 0.4% of the full training data.
MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
Authors: Binqing Wu, Zongjiang Shang, Jianlong Huang, Ling Chen
Venue: CIKM
Year: 2025

Multi-variate time series (MTS) forecasting is crucial for various applications. Existing methods have shown promising results owing to their strong ability to capture intra- and inter-variate dependencies. However, these methods often overlook lead-lag dependencies at multiple grouping scales, failing to capture hierarchical lead-lag effects in complex systems. To this end, we propose MillGNN, a novel graph neural network-based method that learns multiple grouping scale lead-lag dependencies for MTS forecasting, which can comprehensively capture lead-lag effects considering variate-wise and group-wise dynamics and decays. Specifically, MillGNN introduces two key innovations: (1) a scale-specific lead-lag graph learning module that integrates cross-correlation coefficients and dynamic decaying features derived from real-time inputs and time lags to learn lead-lag dependencies for each scale, which can model evolving lead-lag dependencies with statistical interpretability and data-driven flexibility; (2) a hierarchical lead-lag message passing module that passes lead-lag messages at multiple grouping scales in a structured way to simultaneously propagate intra- and inter-scale lead-lag effects, which can capture multi-scale lead-lag effects with a balance of comprehensiveness and efficiency. Experimental results on 11 datasets demonstrate the superiority of MillGNN for long-term and short-term MTS forecasting, compared with 16 state-of-the-art methods.
Robust Multi-Label Learning with Instance-Dependent Label Noise
Authors: You Wu, Yabo Shi, Yizhang Zou, Peipei Li
Venue: CIKM
Year: 2025

Multi-label learning focuses on tasks where each instance is associated with multiple labels. Due to the high cost of obtaining accurate annotations, real-world multi-label datasets often contain noisy labels from crowdsourcing or automated annotation. Noisy multi-label learning has hence been studied to address the label noise problem. However, existing noisy multi-label methods struggle to handle instance-dependent noise (IDN), which is a complex and common type of label noise in practical applications, severely affecting the reliability of multi-label learning. In contrast, existing methods designed for single-label IDN cannot be directly applied to multi-label data. To address these challenges, we propose Robust multi-label learning with Instance-Dependent label noisE (RIDE), a framework for multi-label learning with IDN. Specifically, RIDE first decomposes the observed label matrix into clean and noisy components via a joint low-rank and sparse decomposition. Secondly, a linear sparse mapping from feature space to label space is introduced to explicitly model how instance features induce IDN. Thirdly, to further improve the denoising accuracy, RIDE estimates a noise suppression coefficient for each sample, which weights the sparse regularization term of the noise decomposition. In addition, theoretical analysis is provided to derive upper bounds on the noise estimation and generalization errors. Extensive experiments on benchmark multi-label datasets with varying noise rates show that RIDE outperforms state-of-the-art methods. The code is available at https://github.com/View5U/RIDE.