OpenRaiser/Intern-Atlas huggingface dataset:
NeighSqueeze: Compact Neighborhood Grouping for Efficient Billion-Scale Heterogeneous Graph Learning
Authors: Xinyue Feng, Shuxin Zhong, Jinquan Hang, Yuequn Zhang, Guang Yang, Haotian Wang, Desheng Zhang, Guang Wang
Venue: CIKM
Year: 2025

The rapid growth of online shopping has intensified competition among logistics companies, highlighting the importance of customer expansion, i.e., identifying customers willing to establish long-term contracts. Although existing approaches frame customer expansion as a node classification task using heterogeneous graph learning to capture complex interactions between a customer and other items, it is computationally infeasible to utilize all neighboring interactions on large-scale logistics graphs. Current sub-sampling methods reduce computational load by sampling a small part of neighborhood for training. However, they introduce substantial information loss, particularly affecting high-degree nodes and decreasing predictive accuracy. To address this, we introduce NeighSqueeze, a novel approach that groups structurally and semantically similar nodes, substantially reducing the neighbors count and facilitating full-neighbor learning. NeighSqueeze consists of three modules designed to efficiently and effectively enable node grouping on billion-scale heterogeneous graphs: (1) Structure-tightness-based neighbor filtering reduces the high redundancy and complexity in similarity computations. (2) Hybrid similarity graph construction addresses the difficulty of measuring node similarity at scale; and (3) A two-level grouping strategy resolves the label dominance issue within groups. We evaluate NeighSqueeze on JD Logistics, one of the largest logistics companies in China. Compared with sub-sampling methods, our NeighSqueeze exhibits lower runtime and memory usage with full-neighbor training on the compressed graph, while simultaneously improving average precision over 28.9% in offline evaluation and increase new customer exploration rate by 18.6% in online A/B testing.
Internet of Things Dataset for Human Operator Activity Recognition in Industrial Environment
Authors: Abdur Forkan, Prem Prakash Jayaraman, Clarence Antonmeryl, Federico Montori, Abhik Banerjee, Kaneez Fizza, Dimitrios Georgakopoulos
Venue: CIKM
Year: 2025

In industrial environments, most production-related activities performed by human operators are often complex. Accurate detections of these activities are pivotal as it can greatly help to assess productivity that can lead to improvement in worker training, as well as in other scenarios ensure a safe work environment and reducing injuries. Existing datasets on wearable Internet of Things (IoT) for human activity recognition primarily focuses on general activities, such as walking, running, etc., and therefore, related machine learning models and datasets are not suitable for application to industrial environments. In this paper, we present a novel dataset for classifying human operator activities in a meat processing plant where production line operators use knives to cut, process and produce meat products. Our dataset contains human operator activity data captured using wearable IoT sensors collected from a meat processing production facility. Through extensive experiments using machine and deep learning, we demonstrate that our dataset is effective and useful for detecting different activities of a human operator working in an industrial environment. To the best of our knowledge, this is the only real-world IoT dataset that will be made publicly available to support further research into industrial activities recognition. Our dataset and related experiments are available at https://digitalinnovationlab.github.io/mppdataset.
CALLM: A Framework for Systematic Contrastive Analysis of Large Language Models
Authors: Reinhard Friedrich Fritsch, Adam Jatowt
Venue: CIKM
Year: 2025

This study addresses the challenges of analyzing discrepancies between different large language models (LLMs). To facilitate the automatic exploration of these differences, we propose a novel system called CALLM(Contrastive Analyzer of LLMs) that systematically compares the outputs of two LLM versions based on user-defined queries. The system first generates a hierarchical topic structure rooted in a user-specified query, allowing for an organized comparison of topical categories. Subsequently, it evaluates the text generated by both LLMs to identify differences in knowledge and information presentation. This fully automated approach not only streamlines the identification of differences in knowledge stored by LLMs, model-specific characteristics and performance variations but can also enhance our understanding of architectural and training differences between compared LLMs. Our work contributes to the development of more transparent machine learning models and is meant to foster research in model evaluation and comparative analysis.
ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
Authors: Dongqi Fu, Yada Zhu, Zhining Liu, Lecheng Zheng, Xiao Lin, Zihao Li, Liri Fang, Katherine Tieu, Onkar Bhardwaj, Kommy Weldemariam, Hanghang Tong, Hendrik F. Hamann, Jingrui He
Venue: CIKM
Year: 2025

Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format of time series, recording the climate features, geolocation, time attributes, etc. Recently, much research attention has been paid to the climate benchmarks. In addition to the most common task of weather forecasting, several pioneering benchmark works are proposed for extending the modality, such as domain-specific applications like tropical cyclone intensity prediction and flash flood damage estimation, or climate statement and confidence level in the format of natural language. To further motivate the artificial intelligence development for climate science, in this paper, we first contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns (1) the time series climate data from ERA5, (2) extreme weather events data from NOAA, and (3) satellite image data from NASA HLS based on a unified spatial-temporal granularity. Second, under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks in the proposed ClimateBench-M. The data and code of ClimateBench-M are publicly available at https://github.com/iDEA-iSAIL-Lab-UIUC/ClimateBench-M.
AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence
Authors: Davide Gabrielli, Bardh Prenkaj, Paola Velardi, Stefano Faralli
Venue: CIKM
Year: 2025

We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.
SIoTEc 2025 - 6th edition of ACM Workshop on Secure IoT, Edge and Cloud systems
Authors: Antonino Galletta, Javid Taheri, Giuseppe Di Modica, Annamaria Ficara
Venue: CIKM
Year: 2025

In the last years, we have seen an increase in the number of Artificial Intelligence (AI)-powered applications for information retrieval and data science. This fact led to an increasing reliance on distributed computing infrastructures, including Cloud, Edge, and IoT environments. These architectures enable powerful and scalable solutions but also introduce new security and privacy risks that must be addressed at both the system and data levels. Even a single breach on any of the links of the data-service-infrastructure chain may seriously compromise the security of the end-user application. With such a wide attack surface, security must definitely be approached in a holistic way and addressed in any layer where concerns may potentially arise. SIoTEC solicits novel and innovative ideas, proposals, positions and best practices that address the modelling, design, implementation, and enforcement of security in Cloud/Edge/IoT environments. Workshop website: https://siotec.netsons.org/
Think it Image by Image: Multi-Image Moral Reasoning of Large Vision-Language Models
Authors: Chujie Gao, Yue Huang, Xiangqi Wang, Siyuan Wu, Nitesh V. Chawla, Xiangliang Zhang
Venue: CIKM
Year: 2025

Vision Language Models (VLMs) have demonstrated remarkable success in downstream applications, yet they often exhibit biases, raising ethical concerns. While previous efforts have aimed to evaluate and improve the moral reasoning capabilities of VLMs, existing approaches are limited by simplified, unimodal settings or overly static visual scenarios. We propose a novel multi-image-based dataset pipeline MIST (Moral Inference through Storytelling with Text and Images) designed to assess moral reasoning in complex, dynamic scenarios to address these limitations. To ensure better alignment between these modalities, we introduce the concept of ''text-image flow,'' which seamlessly integrates visual and textual information across complex scenarios. Using this dataset, we evaluate seven widely used VLMs, offering critical insights into their performance in moral reasoning tasks.
HealthGenie: A Knowledge-Driven LLM Framework for Tailored Dietary Guidance
Authors: Fan Gao, Xinjie Zhao, Ding Xia, Zhongyi Zhou, Rui Yang, Jinghui Lu, Hang Jiang, Chanjun Park, Irene Li
Venue: CIKM
Year: 2025

Seeking dietary guidance often requires navigating complex nutritional knowledge while considering individual health needs. To address this, we present HealthGenie, an interactive platform that leverages the interpretability of knowledge graphs (KGs) and the conversational power of large language models (LLMs) to deliver tailored dietary recommendations alongside integrated nutritional visualizations for fast, intuitive insights. Upon receiving a user query, HealthGenie performs intent refinement and maps user's needs to a curated nutritional knowledge graph. The system then retrieves and visualizes relevant subgraphs, while offering detailed, explainable recommendations. Users can interactively adjust preferences to further tailor results. A within-subject study and quantitative analysis show that HealthGenie reduces cognitive load and interaction effort while supporting personalized, health-aware decision-making.
FediData: A Comprehensive Multi-Modal Fediverse Dataset from Mastodon
Authors: Min Gao, Haoran Du, Wen Wen, Qiang Duan, Xin Wang, Yang Chen
Venue: CIKM
Year: 2025

Recently, decentralized online social networks (DOSNs) such as Mastodon have emerged quickly, bringing new opportunities for studies in user behavior modeling and multi-modal learning. However, their decentralized architecture presents two key challenges: 1) Distributed data and inconsistent access strategies across several individual instances make a unified collection difficult; 2) user-generated content (UGC) contains multiple modalities while lacking standard organization and high-quality annotation. To address these issues, we constructed FediData, a comprehensive multi-modal dataset from Mastodon. Our dataset integrates user profiles, text, images, and social interactions. To validate FediData's usefulness, we designed and analyzed several tasks and systematically evaluated the performance of existing state-of-the-art methods. Our analysis reveals the unique challenges of DOSNs and highlights the value of FediData in DOSN-related studies. We believe FediData could serve as a foundational dataset for advancing user behavior analytics, multi-modal learning, and future decentralized web research. All data and documentation are available in a Zenodo repository at https://zenodo.org/records/15621243 (DOI: 10.5281/zenodo.15621243).
DT-FedSDC: A Dual-Target Federated Framework with Semantic Enhancement and Disentangled Contrastive Learning for Cross-Domain Recommendation
Authors: Shanyang Gao, Shanfeng Wang, Lanyu Yao, Jianzhao Li, Zhao Wang, Maoguo Gong, Ke Pan
Venue: CIKM
Year: 2025

Federated cross-domain recommendation aims to alleviate the problem of data sparsity and enable collaborative modeling of user behavior data from different platforms or institutions while ensuring data privacy. Most existing federated cross-domain recommendation methods rely on item IDs for modeling, ignoring the mining and utilization of item semantic information. In addition, due to the heterogeneity of data between different domains, the model is prone to domain bias and feature coupling problems during the aggregation process, which negatively impacts the recommendation performance. This paper proposes a dual-target federated cross-domain recommendation framework with semantic enhancement and disentangled contrastive learning. First, to utilize semantic information of items, item IDs features and text semantic features are jointly fused to enhance the item embedding representations. Second, we propose a user representation decoupling mechanism to explicitly decouple users preferences into shared and domain-specific preferences, thereby alleviating domain bias and feature coupling problems. Furthermore, we design a cross-domain contrastive learning module on the server side to enhance the consistency and transferability of shared representations between user representations across different domains. Experimental results show that the proposed algorithm performs significantly better than existing optimal methods on multiple real-world datasets, demonstrating its excellent performance in federated cross-domain recommendations.
LangPTune: Optimizing Language-based User Profiles for Recommendation
Authors: Zhaolin Gao, Joyce Zhou, Yijia Dai, Thorsten Joachims
Venue: CIKM
Year: 2025

Recent works have shown increasing interest in using natural language-based user profiles for recommender systems, as they offer greater transparency and interpretability compared to traditional embedding-based methods. Most existing approaches rely on zero-shot inference with large language models (LLMs) to generate these profiles, but the resulting quality remains insufficient, leading to suboptimal recommendation performance. In this paper, we present LangPTune, the first end-to-end training framework designed to directly optimize LLM-generated user profiles for recommendation tasks. By explicitly training the LLM for the recommendation objective, our approach significantly outperforms zero-shot baselines. Evaluations across training setups and benchmarks show that LangPTune not only exceeds the performance of zero-shot methods but also matches the performance of state-of-the-art embedding-based baselines. Additionally, we assess whether our training framework maintains the interpretability of user profiles, using both GPT-4 simulations and crowdworker studies.
Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting
Authors: Yifan Gao, Boming Zhao, Haocheng Peng, Hujun Bao, Jiashu Zhao, Zhaopeng Cui
Venue: CIKM
Year: 2025

Recent advances in deep learning have significantly boosted performance in multivariate time series forecasting (MTSF). While many existing approaches focus on capturing inter-variable (a.k.a. channel-wise) correlations to improve prediction accuracy, the temporal dimension, particularly its rich structural and contextual information, remains underexplored. In this paper, we propose BIM3, a novel framework that integrates BIdirectional temporal-aware modeling with Multi-Scale Mixture-of-Experts for MTSF. First, unlike existing methods that treat historical and future temporal information independently, we introduce a novel Timestamp Dual Cross-Attention Module, which employs a symmetric cross-attention mechanism to explicitly capture bidirectional temporal dependencies through timestamp interactions. Second, to address the complex and scale-varying temporal patterns commonly found in multivariate time series, we move beyond recent multi-scale forecasting models that share parameters across all channels and fail to capture channel-specific dynamics. Instead, we design a Multi-Scale Feature Extract Mixture-of-Experts module that adaptively routes time series to specialized experts based on their temporal characteristics. Extensive experiments on multiple real-world datasets show that BIM3 consistently outperforms state-of-the-art methods, highlighting its effectiveness in capturing both temporal structure and inter-variable diversity.
STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions
Authors: Amirhossein Ghaffari, Huong Nguyen, Lauri Lovén, Ekaterina Gilman
Venue: CIKM
Year: 2025

Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: https://github.com/Ahghaffari/stm_graph and https://github.com/tuminguyen/stm_graph_gui.
LLM-OFA: On-the-Fly Adaptation of Large Language Models to Address Temporal Drift Across Two Decades of News
Authors: Pouya Ghahramanian, Sepehr Bakhshi, Fazli Can
Venue: CIKM
Year: 2025

We investigate the problem of on-the-fly adaptation (OFA) with online feedback for large language models (LLMs) in the context of temporally evolving data. In this setting, each incoming instance-or a small batch- is first processed for inference, and its true label is revealed immediately after prediction, allowing the model to be updated in a sequential, single-pass manner. While pre-trained LLMs achieve state-of-the-art results across NLP tasks, they often struggle to generalize under dynamic distribution shifts-particularly in continuously evolving environments. Despite the importance of this problem, existing research on online adaptation of LLMs remains limited, and there is a lack of large-scale benchmarks for evaluating such methods. To address these gaps, we introduce 1M-News, a large-scale benchmark of one million New York Times headlines spanning two decades, and benchmark six state-of-the-art LLMs by fine-tuning them on the first 10 years and applying OFA on the following 10 years. To improve adaptation performance, we develop Adaptimizer, the first optimizer specifically designed for OFA, enabling rapid and stable model updates under temporal distribution shift. Adaptimizer maintains two sets of weights-fast and slow-balancing rapid adaptation with long-term stability and generalization across the stream. Our experiments demonstrate that OFA with Adaptimizer achieves consistent improvements over static baselines. All code and data are publicly available at https://github.com/pouyaghahramanian/LLM-OFA.
Approximating Gradient-Based Influence for Scalable Instruction Data Selection
Authors: Mohammad Gharehhasanloo, Yueting Chen, Nick Koudas, Xiaohui Yu
Venue: CIKM
Year: 2025

Instruction Tuning (IT) is crucial for enhancing Large Language Models (LLMs), but training on all available instructions is often unnecessary and computationally costly. Recent studies show that small, well-chosen subsets can match or exceed full dataset performance, motivating efficient data selection techniques. While gradient-based methods like LESS estimate sample influence effectively, they are expensive due to per-sample gradient computation. We propose Approx-LESS, a scalable alternative that computes LoRA-based gradient features for a small fraction of the samples and trains regression models to predict influence scores for the rest. This enables selection and tuning on the most impactful samples. On three validation sets with a fixed 270K instruction corpus, Approx-LESS outperforms applicable baselines and closely matches LESS, reducing gradient extraction time by over 3x. It also shows high sample selection overlap with LESS, making it an effective, low-cost method for influence-based instruction tuning.
High-Order Moments Conditional Domain Adaptation Networks for Wearable Human Activity Recognition
Authors: Indrajeet Ghosh, Garvit Chugh, Abu Zaher Md Faridee, Nirmalya Roy
Venue: CIKM
Year: 2025

Developing scalable wearable human activity recognition (wHAR) models is challenging due to domain shifts that substantially degrade performance across downstream tasks. Unsupervised domain adaptation (UDA) seeks to improve generalization by transferring knowledge from labeled source domains to unlabeled target domains. However, conventional UDA methods primarily align marginal feature distributions while neglecting feature-label dependencies, often leading to negative transfer and sub-optimal performance. Motivated by these limitations, we propose a novel optimization framework that tackles two key challenges: (i) generating reliable pseudo-labels for the unlabeled target domain and (ii) minimizing conditional discrepancies across domains. To address (i), we employ temperature-based entropy minimization (TEM), which calibrates prediction confidence by scaling logits with a temperature parameter to produce robust pseudo-labels. For (ii), we introduce a polynomial kernel-based cross-covariance (PkCC) loss, a high-order statistics-driven approach that maps features into a reproducing kernel hilbert space (RKHS) to capture richer feature-label dependencies and reduce conditional distribution gaps between domains. In addition, we demonstrate that CoDAN readily extends to partial UDA (pUDA), where the target label space is a subset of the source, and extensive evaluations on public wHAR datasets with diverse label spaces validate its superior performance over state-of-the-art methods in both UDA and pUDA scenarios.