OpenRaiser/Intern-Atlas huggingface dataset:
DYCOR: Capturing Hidden Stock Relationships for Stock Trend Prediction
Authors: Kangmin Choi, Geon Shin, Jungwoo Yang, Hyunjoon Kim
Venue: CIKM
Year: 2025

Stock trend prediction, the task of forecasting future trends of stocks from their historical feature sequences, remains highly challenging due to the complex and dynamic nature of financial markets. In reality, stocks form diverse relationships that transcend traditional sector boundaries as market conditions evolve, i.e., stocks within the same sector may display different trends, while those in different sectors often exhibit similar movements. However, most existing stock prediction methods rely on predefined static relationships, lacking flexibility to adapt to changing market dynamics. Furthermore, objectives widely adopted in prior work have limitations in capturing complex patterns and relationships in stock market data. To address these limitations, we propose DYCOR, a novel stock trend prediction method that integrates two key innovations: (i) dynamic stock clustering, which captures market characteristics without relying on predefined relationship data by adaptively discovering hidden stock relationships; and (ii) correlation-aware training, which aligns predicted and ground-truth stock trends by reflecting their correlations in a fine-grained manner. We evaluate DYCOR on three datasets NASDAQ, NYSE, and S&P 500 widely used in existing research, and this method demonstrates superior performance across correlation-based and retrieval-based metrics compared to state-of-the-art baseline methods, while maintaining competitive runtime efficiency.
Contrastive ECOC: Learning Output Codes for Adversarial Defense
Authors: Che-Yu Chou, Hung-Hsuan Chen
Venue: CIKM
Year: 2025

Although one-hot encoding is commonly used for multiclass classification, it is not always the most effective encoding mechanism. Error Correcting Output Codes (ECOC) address multiclass classification by mapping each class to a unique codeword used as a label. Traditional ECOC methods rely on manually designed or randomly generated codebooks, which are labor-intensive and may yield suboptimal, dataset-agnostic results. This paper introduces three models for automated codebook learning based on contrastive learning, allowing codebooks to be learned directly and adaptively from data. Across four datasets, our proposed models demonstrate superior robustness to adversarial attacks compared to two baselines. The source is available at GitHub.
The ReQAP System for Question Answering over Personal Information
Authors: Philipp Christmann, Gerhard Weikum
Venue: CIKM
Year: 2025

Personal information is abundant on users' devices, from structured data in calendar, shopping records or fitness tools, to unstructured contents in mail and social media posts. This works presents the ReQAP system that supports users with answers for complex questions that involve filters, joins and aggregation over heterogeneous sources. The unique trait of ReQAP is that it recursively decomposes questions and incrementally builds an operator tree for execution. Both the question interpretation and the individual operators make smart use of light-weight language models, with judicious fine-tuning. The demo showcases the rich functionality for advanced user questions, and also offers detailed tracking of how the answers are computed by the operators in the execution tree. Being able to trace answers back to the underlying sources is vital for human comprehensibility and user trust in the system.
Explain and Monitor Deep Learning Models for Computer Vision using Obz AI
Authors: Neo Christopher Chung, Jakub Binda
Venue: CIKM
Year: 2025

Deep learning has transformed computer vision (CV), achieving outstanding performance in classification, segmentation, and related tasks. Such AI-based CV systems are becoming prevalent, with applications spanning from medical imaging to surveillance. State of the art models such as convolutional neural networks (CNNs) and vision transformers (ViTs) are often regarded as ''black boxes,'' offering limited transparency into their decision-making processes. Despite a recent advancement in explainable AI (XAI), explainability remains underutilized in practical CV deployments. A primary obstacle is the absence of integrated software solutions that connect XAI techniques with robust knowledge management and monitoring frameworks. To close this gap, we have developed Obz AI, a comprehensive software ecosystem designed to facilitate state-of-the-art explainability and observability for vision AI systems. Obz AI provides a seamless integration pipeline, from a Python client library to a full-stack analytics dashboard. With Obz AI, a machine learning engineer can easily incorporate advanced XAI methodologies, extract and analyze features for outlier detection, and continuously monitor AI models in real time. By making the decision-making mechanisms of deep models interpretable, Obz AI promotes observability and responsible deployment of computer vision systems.
MU-OT: Effective and Unified Machine Unlearning with Optimal Transport for Feature Realignment
Authors: Sangjun Chung, Simon S. Woo
Venue: CIKM
Year: 2025

Machine unlearning has emerged as a significant research topic in response to the increasing demands for data privacy and compliance with privacy regulations. The main challenge is to eliminate the influence of a specific subset of training data from a pretrained model while preserving the model's performance on the retain set without retraining the model from scratch. In this paper, we propose a novel efficient unlearning framework based on Optimal Transport, which can effectively work on both class-wise and instance-wise unlearning tasks. By analyzing and comparing the feature spaces of the original and retrained models, we formulate the unlearning problem as a distribution alignment task between the forget set and the retain set. We guide the feature distribution of the forget set, which initially forms distinct and structured patterns, to align with that of the retain set. Extensive experiments on three public benchmark datasets demonstrate its superior effectiveness compared to previous state-of-the-art methods.
ClimBurst: A Dynamic Visualization Tool to Display Climatological Anomalies over Time and Space
Authors: Guillaume Coulaud, Benoit Lange, Dennis Shasha, Audrey Brouillet, Reza Akbarinia, Florent Masseglia
Venue: CIKM
Year: 2025

Detecting abnormal climate events across temporal and spatial scales is crucial to the understanding of local and regional climate trends. This demonstration introduces ClimBurst, a dynamic tool to detect climate bursts, which are unusually high or low values of one or more climate variables over some time interval. ClimBurst detects bursts without prior assumptions about their temporal duration. The demonstration will allow users to interact directly with our system to see both a summary showing the presence/absence of bursts over a user-specified year and spatial range. The demonstration will also allow users to perform time-travel queries to see how bursts propagate over space and time.
ERASURE: A Modular and Extensible Framework for Machine Unlearning
Authors: Andrea D&apos, Angelo, Claudio Savelli, Gabriele Tagliente, Flavio Giobergia, Elena Baralis, Giovanni Stilo
Venue: CIKM
Year: 2025

Machine Unlearning (MU) is an emerging research area that enables models to selectively forget specific data, a critical requirement for privacy compliance (e.g., GDPR, CCPA) and security. However, the lack of standardized benchmarks makes evaluating and developing unlearning methods difficult. To address this gap, we introduce ERASURE, a benchmarking and development framework designed to systematically assess MU techniques. ERASURE provides a modular, extensible, open-source environment with real-world datasets and standardized unlearning measures. The framework is designed with configuration-driven workflows and an inversion of control architecture, allowing integration of new datasets, models, and evaluation measures. ERASURE advances trustworthy AI research as a tool for researchers to develop and benchmark new MU methods.
H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems
Authors: Huangyu Dai, Lingtao Mao, Ben Chen, Zihan Wang, Zihan Liang, Ying Han, Chenyi Lei, Han Li
Venue: CIKM
Year: 2025

Hotword customization is crucial in ASR to enhance the accuracy of domain-specific terms. It has been primarily driven by the advancements in traditional models and Audio large language models (LLMs). However, existing models often struggle with large-scale hotwords, as the recognition rate drops dramatically with the number of hotwords increasing. In this paper, we introduce a novel hotword customization system that utilizes a hotword pre-retrieval module (H-PRM) to identify the most relevant hotword candidate by measuring the acoustic similarity between the hotwords and the speech segment. This plug-and-play solution can be easily integrated into traditional models such as SeACo-Paraformer, significantly enhancing hotwords post-recall rate (PRR). Additionally, we incorporate H-PRM into Audio LLMs through a prompt-based approach, enabling seamless customization of hotwords. Extensive testing validates that H-PRM can outperform existing methods, showing a new direction for hotword customization in ASR.
FedGVD: Efficient Federated Graph Learning via Unidirectional Distillation with Dynamic Virtual Nodes
Authors: Zhehao Dai, Guojiang Shen, Yuyue Hu, Jiaxin Du, Xiao Han, Xiangjie Kong
Venue: CIKM
Year: 2025

Federated Graph Learning (FGL) has emerged as a key paradigm for distributed graph machine learning, enabling cross-domain graph collaborative modeling while preserving data privacy. However, existing methods face two major bottlenecks: the structural heterogeneity discrepancy of graph data among clients weakens the generalization ability of the global model; and model heterogeneity leads to inefficient knowledge sharing and complex global aggregation. To address these issues, we propose FedGVD, an efficient framework that constructs a global perspective through data condensation and server-side virtual node generation, which not only preserves the semantic equivalence of the original data but also avoids privacy leakage. Subsequently, by distributing low-dimensional generalizable knowledge for unidirectional distillation, FedGVD enables local models to absorb global knowledge without transmitting local parameters, thus breaking through the challenges of data and structural heterogeneity as well as model heterogeneity. This innovative approach ensures privacy-preserving and efficient federated graph collaboration. Experiments show that FedGVD maintains excellent performance in heterogeneous model scenarios while significantly improving communication efficiency, offering a new approach for privacy-preserving collaborative modeling in FGL. The code is available at https://github.com/Jasonxx4/FedGVD.
SparseKmeans: Efficient K-means Clustering For Sparse Data
Authors: Khoi Nguyen Pham Dang, He-Zhe Lin, Chih-Jen Lin
Venue: CIKM
Year: 2025

We introduce SparseKmeans, the first Python package for fast K-means clustering on high-dimensional sparse data. Most existing K-means implementations, such as scikit-learn, are only optimized for dense data and do not run efficiently on sparse inputs. In this work, we thoroughly investigate how to accelerate widely used K-means algorithms on sparse data via matrix operations. In particular, we propose a new design of Elkan's method that aggregates distance computations and reduces fragmented memory access. By analyzing the structure of key matrices and leveraging highly optimized sparse matrix libraries, SparseKmeans achieves up to 9x speedup over scikit-learn. The package is available at https://github.com/cjlin1/sparsekmeans.
TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates
Authors: Afrin Dange, Sunita Sarawagi
Venue: CIKM
Year: 2025

Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance in univariate forecasting on new time series simply by conditioning on a brief history of past values. Their success demonstrates that large-scale pretraining across diverse domains can acquire the inductive bias to generalize from temporal patterns in a brief history. However, most TSFMs are unable to leverage covariates-future-available exogenous variables critical for accurate forecasting in many applications-due to their domain-specific nature and the lack of associated inductive bias. We propose TFMAdapter, a lightweight, instance-level adapter that augments TSFMs with covariate information without fine-tuning. Instead of retraining, TFMAdapter operates on the limited history provided during a single model call, learning a non-parametric cascade that combines covariates with univariate TSFM forecasts. However, such learning would require univariate forecasts at all steps in the history, requiring too many calls to the TSFM. To enable training on the full historical context while limiting TSFM invocations, TFMAdapter uses a two-stage method: (1) generating pseudo-forecasts with a simple regression model, and (2) training a Gaussian Process regressor to refine predictions using both pseudo- and TSFM forecasts alongside covariates. Extensive experiments on real-world datasets demonstrate that TFMAdapter consistently outperforms both foundation models and supervised baselines, achieving a 24-27% improvement over base foundation models with minimal data and computational overhead. Our results highlight the potential of lightweight adapters to bridge the gap between generic foundation models and domain-specific forecasting needs.
When Words Can't Capture It All: Towards Video-Based User Complaint Text Generation with Multimodal Video Complaint Dataset
Authors: Sarmistha Das, R. E. Zera Marveen Lyngkhoi, Kirtan Jain, Vinayak Goyal, Sriparna Saha, Manish Gupta
Venue: CIKM
Year: 2025

While there exists a lot of work on explainable complaint mining, articulating user concerns through text or video remains a significant challenge, often leaving issues unresolved. Users frequently struggle to express their complaints clearly in text but can easily upload videos depicting product defects (e.g., vague text such as 'worst product' paired with a 5-second video depicting a broken headphone with the right earcup). This paper formulates a new task in the field of complaint mining to aid the common users' need to write an expressive complaint, which is Complaint Description from Videos (CoD-V) (e.g., to help the above user articulate her complaint about the defective right earcup). To this end, we introduce ComVID, a video complaint dataset containing 1,175 complaint videos and the corresponding descriptions, also annotated with the emotional state of the complainer. Additionally, we present a new complaint retention (CR) evaluation metric that discriminates proposed (CoD-V) task against standard video summary generation and description task. To strengthen this initiative, we introduce a multimodal Retrieval-Augmented Generation (RAG) embedded VideoLLaMA2-7b model, designed to generate complaints while accounting for the user's emotional state. We conduct a comprehensive evaluation of several Video Language Models on several tasks (pre-trained and fine-tuned versions) with a range of established evaluation metrics, including METEOR, perplexity, and the Coleman-Liau readability score, among others. Our study lays the foundation for a new research direction to provide a platform for users to express complaints through video. Dataset and resources are available at: https://github.com/sarmistha-D/CoD-V.
+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking
Authors: Xingyu Deng, Xi Wang, Mark Stevenson
Venue: CIKM
Year: 2025

Identification of appropriate supporting evidence is critical to the success of scientific fact checking. However, existing approaches rely on off-the-shelf Information Retrieval algorithms that rank documents based on relevance rather than the evidence they provide to support or refute the claim being checked. This paper proposes +VeriRel which includes verification success in the document ranking. Experimental results on three scientific fact checking datasets (SciFact, SciFact-Open and Check-Covid) demonstrate consistently leading performance by +VeriRel for document evidence retrieval and a positive impact on downstream verification. This study highlights the potential of integrating verification feedback to document relevance assessment for effective scientific fact checking systems. It shows promising future work to evaluate fine-grained relevance when examining complex documents for advanced scientific fact checking.
Mitigating Latent Confounding Bias in Recommender Systems
Authors: Jianfeng Deng, Qingfeng Chen, Debo Cheng, Xiaojing Du, Jiuyong Li, Lin Liu
Venue: CIKM
Year: 2025

Recommender systems are crucial for providing personalised experiences, but their effectiveness is often undermined by confounding bias, particularly in the presence of latent confounders. Existing debiasing methods typically address only one type of latent confounding bias, often ignoring the complex interactions caused by latent confounders, such as those between items and user feedback, and between item exposure and user feedback. To tackle these challenges, we propose a novel Deep Instrumental Variables (IV) approach for debiased representation learning in Recommendation Systems, referred to as DIVERS. Specifically, DIVERS leverages user feature embeddings as IVs to mitigate the confounding bias between items and user feedback caused by latent confounders, and combines the debiased item embeddings with an item exposure vector to generate a reconstructed item exposure vector. Moreover, DIVERS employs an identifiable Variational Auto-Encoder (iVAE) to infer identifiable representations by utilising information from both the original and reconstructed item exposure vectors, effectively addressing the confounding bias introduced by latent confounders between item exposure and user feedback. Additionally, we provide theoretical analyses to demonstrate the soundness of using IV and the identifiability of the representation learned by DIVERS. Extensive experiments on both synthetic and real-world datasets confirm that DIVERS outperforms state-of-the-art models in reducing bias and providing reliable recommendations. Our source code is available at: https://github.com/djf-web/DIVERS.
Correlation-aware Online Change Point Detection
Authors: Chengyuan Deng, Zhengzhang Chen, Xujiang Zhao, Haoyu Wang, Junxiang Wang, Jie Gao, Haifeng Chen
Venue: CIKM
Year: 2025

Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of change can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics. We introduce Rio-CPD, a correlation-aware online change point detection framework that integrates the Riemannian geometry of the manifold of symmetric positive definite matrices with the cumulative sum (CUSUM) statistic for detecting change points. Rio-CPD employs a novel CUSUM design by computing the geodesic distance between current observations and the Fréchet mean of prior observations. With appropriate choices of Riemannian metrics, Rio-CPD offers a simple yet effective and computationally efficient algorithm. We also provide a theoretical analysis on standard metrics for change point detection within Rio-CPD. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay, and efficiency.
DDE-CLIP: Detail-Guided Dual-Modal Enhancement for Zero-Shot Anomaly Detection
Authors: Zehao Deng, Qingzhi Ma, An Liu
Venue: CIKM
Year: 2025

Zero-shot Anomaly Detection (ZSAD) is an emerging task in industrial settings. It aims to detect anomalies in a target dataset without training samples, which is crucial for sample scarcity and data privacy. Existing methods largely rely on CLIP, leveraging its internal knowledge to detect anomalies. However, due to its pre-training on natural image-text pairs, CLIP suffers from domain shift, favoring global semantics over fine-grained defect detection in industrial images. Furthermore, most existing methods employ fixed text prompt to guide the model, which is difficult to describe diverse and unseen anomalies, leading to poor accuracy. To address these limitations, we propose a Detail-guided Dual-modal Enhancement Model (DDE-CLIP) for the ZSAD task. Firstly, we designed the Detail Feature Reinforcement Module (DFRM) to capture local representations of minute defects. Its specialized design effectively enhances the model's perception of fine-grained anomalies and enables the pre-trained CLIP model to better adapt to the unique visual characteristics of industrial images. Subsequently, we introduced the Visual-guided Text Refinement Module (VTRM), which can dynamically optimize text prompts based on the input image's visual content (particularly the detail features captured by DFRM). This ensures the accurate reflection of text prompts on specific semantics of various defects, thereby significantly enhancing the alignment between vision and text for unseen anomalies. Overall, our DDE-CLIP uses detail features to enhance both image and text modalities, effectively addressing the challenges of ZSAD. Extensive experiments on 7 real-world industrial product datasets demonstrate that DDE-CLIP exhibits superior detection and localization capabilities compared to other methods. The code is available at https://github.com/zhushengxinyue/DDE-CLIP.
DIVAgent: A Diversified Search Agent that Mimics the Human Search Process
Authors: Zhirui Deng, Jingfen Qiao, Zhicheng Dou, Ji-Rong Wen, Maarten de Rijke
Venue: CIKM
Year: 2025

Search result diversification plays a crucial role in addressing query ambiguity and multi-faceted information needs by reducing redundancy across documents. While previous supervised approaches can achieve superior performance, they require costly, large-scale annotated data. In contrast, unsupervised methods are more flexible and training-free but rely on manually designed ranking functions, often leading to suboptimal performance. Inspired by how humans explore diverse information during real-world searching, we propose a diversified search agent DIVAgent to combine the advantages of supervised and unsupervised methods. DIVAgent introduces LLMs as the ''brain'' to reason over complex and diverse search results and delineate human cognitive processes into a workflow tailored for search result diversification. Our search agent first identifies potential user intents and then analyzes the alignment of each document to the intents via an intent-aware module. To guide the generation of diversified document rankings, we design an intent-guided ranker that explicitly links documents to their dominant intents while performing greedy document selection. Experimental results demonstrate that DIVAgent significantly outperforms existing unsupervised baselines and achieves competitive performance with supervised models, highlighting the promise of LLMs for diversified ranking in realistic search scenarios.