OpenRaiser/Intern-Atlas huggingface dataset:
How Fair is FAIR? Understanding LOD Cloud FAIRness Through Correlation Patterns
Authors: Maria Angela Pellegrino, Gabriele Tuozzo
Venue: CIKM
Year: 2025

While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) and data quality dimensions are widely used to evaluate Linked Data, their interdependencies remain largely unexplored. This paper is grounded on a systematic integration of these two frameworks by mapping data quality dimensions to FAIR sub-principles, revealing how individual features---such as endpoint availability, metadata richness, or use of standard vocabularies---can simultaneously contribute to multiple FAIR goals. Building on this mapping, this paper reports a large-scale, data-driven, longitudinal study of 1,445 datasets from the LOD Cloud extending KGHeartBeat, an open-source quality assessment framework. This paper quantifies FAIRness at the sub-principle level and computes correlation patterns across five temporal snapshots and nine topical domains. The reported findings reveal that most correlations are positive and statistically significant but vary across time and domain, with only a few stable or persistent relationships. Strong inter-principle correlations-such as those linking metadata standards and security transparency-emerge over time, while intra-principle coherence is often weak. These insights offer concrete guidance for improving FAIR compliance, highlight the importance of domain-aware evaluation, and support the development of more holistic and reproducible FAIR assessment strategies for Linked Data ecosystems.
Dialogues Aspect-based Sentiment Quadruple Extraction via Structural Entropy Minimization Partitioning
Authors: Kun Peng, Cong Cao, Hao Peng, Zhifeng Hao, Lei Jiang, Kongjing Gu, Yanbing Liu, Philip S. Yu
Venue: CIKM
Year: 2025

Dialogues Aspect-based Sentiment Quadruple Extraction (DiaASQ) aims to extract all target-aspect-opinion-sentiment quadruples from a given multi-round, multi-participant dialogue. Existing methods typically learn word relations across entire dialogues, assuming a uniform distribution of sentiment elements. However, we find that dialogues often contain multiple semantically independent sub-dialogues without clear dependencies between them. Therefore, learning word relationships across the entire dialogue inevitably introduces additional noise into the extraction process. To address this, our method focuses on partitioning dialogues into semantically independent sub-dialogues. Achieving completeness while minimizing these sub-dialogues presents a significant challenge. Simply partitioning based on reply relationships is ineffective. Instead, we propose utilizing a structural entropy minimization algorithm to partition the dialogues. This approach aims to preserve relevant utterances while distinguishing irrelevant ones as much as possible. Furthermore, we introduce a two-step framework for quadruple extraction: first extracting individual sentiment elements at the utterance level, then matching quadruples at the sub-dialogue level. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in DiaASQ with much lower computational costs.
Expert-Guided Diffusion Planner for Auto-Bidding
Authors: Yunshan Peng, Wenzheng Shu, Jiahao Sun, Yanxiang Zeng, Jinan Pang, Wentao Bai, Yunke Bai, Xialong Liu, Peng Jiang
Venue: CIKM
Year: 2025

Auto-bidding is widely used in advertising systems, serving a diverse range of advertisers. Generative bidding is increasingly gaining traction due to its strong planning capabilities and generalizability. Unlike traditional reinforcement learning-based bidding, generative bidding does not depend on the Markov Decision Process (MDP), thereby exhibiting superior planning performance in long-horizon scenarios. Conditional diffusion modeling approaches have shown significant promise in the field of auto-bidding. However, relying solely on return as the optimality criterion is insufficient to guarantee the generation of truly optimal decision sequences, as it lacks personalized structural information. Moreover, the auto-regressive generation mechanism of diffusion models inherently introduces timeliness risks. To address these challenges, we introduce a novel conditional diffusion modeling approach that integrates expert trajectory guidance with a skip-step sampling strategy to improve generation efficiency. The efficacy of this method has been demonstrated through comprehensive offline experiments and further substantiated by statistically significant outcomes in online A/B testing, yielding an 11.29% increase in conversions and a 12.36% growth in revenue relative to the baseline.
Data-centric Prompt Tuning for Dynamic Graphs
Authors: Yufei Peng, Cheng Yang, Zhengjie Fan, Chuan Shi
Venue: CIKM
Year: 2025

Dynamic graphs have attracted increasing attention due to their ability to model complex and evolving relationships in real-world scenarios. Traditional approaches typically pre-train models using dynamic link prediction and directly apply the resulting node temporal embeddings to specific downstream tasks. However, the significant differences among downstream tasks often lead to performance degradation, especially under few-shot settings. Prompt tuning has emerged as an effective solution to this problem. Existing prompting methods are often strongly coupled with specific model architectures or pretraining tasks, which makes it difficult to adapt to recent or future model designs. Moreover, their exclusive focus on modifying node or temporal features while neglecting spatial structural information leads to limited expressiveness and degraded performance. To address these limitations, we propose DDGPrompt, a data-centric prompting framework designed to effectively refine pre-trained node embeddings at the input data level, enabling better adaptability to diverse downstream tasks. We first define a unified node expression feature matrix that aggregates all relevant temporal and structural information of each node, ensuring compatibility with a wide range of dynamic graph models. Then, we introduce three prompt matrices (temporal bias, edge weight, and feature mask) to adjust the feature matrix completely, achieving task-specific adaptation of node embeddings. We evaluate DDGPrompt under a strict few-shot setting on four public dynamic graph datasets. Experimental results demonstrate that our method significantly outperforms traditional methods and prompting approaches in scenarios with limited labels and cold-start conditions.
DistillCaps: Enhancing Audio-Language Alignment in Captioning via Retrieval-Augmented Knowledge Distillation
Authors: Thinh Pham, Nghiem Tuong Diep, Lizi Liao, Binh T. Nguyen
Venue: CIKM
Year: 2025

Automated audio captioning (AAC) benefits from incorporating external context to interpret complex sounds, but doing so with retrieval-augmented generation (RAG) at inference is sometimes infeasible due to data availability or incurs significant latency and complexity. We propose DistillCaps, a novel training-time framework that leverages RAG to guide knowledge distillation for improved audio-language alignment, while lessening the reliance on retrieval during inference. In our framework, a RAG-equipped teacher model retrieves relevant textual information (e.g., similar captions) for each audio clip and uses it for training to generate context-enriched captions. Simultaneously, a student model is trained to imitate this teacher, learning to produce high-quality captions from audio alone. We further introduce a Fast Fourier Transform (FFT) adapter in the audio encoder to inject frequency-domain features, enhancing the quality of audio representations before feeding them into the language model. The result is an efficient captioning model that retains RAG's contextual benefits without its deployment overhead. On standard AAC benchmarks (AudioCaps and Clotho), DistillCaps achieves performance competitive with or exceeding prior RAG-based systems despite using no retrieval at test time. Notably, our distilled model matches state-of-the-art captioning results under real-time settings, and when optionally allowing retrieval, it even outperforms previous models by up to 4% on the Clotho benchmark on the in-distribution setting, demonstrating the effectiveness of RAG-guided distillation for audio-language alignment. Code and dataset are available here. https://github.com/pgthinh/DistillCaps.
Oblivious Johnson-Lindenstrauss embeddings for compressed Tucker decompositions
Authors: Matthew Pietrosanu, Bei Jiang, Linglong Kong
Venue: CIKM
Year: 2025

Emphasis in the tensor literature on random embeddings (tools for low-distortion dimension reduction) for the canonical polyadic (CP) tensor decomposition has left analogous results for the more expressive Tucker decomposition comparatively lacking. This work establishes general Johnson--Lindenstrauss (JL) guarantees for the estimation of Tucker decompositions when an oblivious random embedding is applied along each mode. When these embeddings are drawn from a JL-optimal family, the decomposition can be estimated within ε relative error under restrictions on the embedding dimension that are in line with recent CP results. We implement a higher-order orthogonal iteration (HOOI) decomposition algorithm with structured random embeddings to demonstrate the practical benefits of this approach and its potential to improve the accessibility of otherwise prohibitive tensor analyses. On moderately large face image and fMRI neuroimaging datasets, empirical results show that substantial dimension reduction is possible with minimal increase in reconstruction error relative to traditional HOOI (łeq15% larger error, 50% lower computation time for large models with 50% dimension reduction along each mode). Especially for large tensors, our method outperforms traditional higher-order singular value decomposition (HOSVD) and recently proposed TensorSketch methods.
Evaluating Robustness of LLMs in Question Answering on Multilingual Noisy OCR Data
Authors: Bhawna Piryani, Jamshid Mozafari, Abdelrahman Abdallah, Antoine Doucet, Adam Jatowt
Venue: CIKM
Year: 2025

Optical Character Recognition (OCR) plays a crucial role in digitizing historical and multilingual documents, yet OCR errors - imperfect extraction of text, including character insertion, deletion, and substitution can significantly impact downstream tasks like question-answering (QA). In this work, we conduct a comprehensive analysis of how OCR-induced noise affects the performance of Multilingual QA Systems. To support this analysis, we introduce a multilingual QA dataset MultiOCR-QA, comprising 50K question-answer pairs across three languages, English, French, and German. The dataset is curated from OCR-ed historical documents, which include different levels and types of OCR noise. We then evaluate how different state-of-the-art Large Language Models (LLMs) perform under different error conditions, focusing on three major OCR error types. Our findings show that QA systems are highly prone to OCR-induced errors and perform poorly on noisy OCR text. By comparing model performance on clean versus noisy texts, we provide insights into the limitations of current approaches and emphasize the need for more noise-resilient QA systems in historical digitization contexts.
Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Authors: Claudio Pomo, Matteo Attimonelli, Danilo Danese, Fedelucio Narducci, Tommaso Di Noia
Venue: CIKM
Year: 2025

Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear whether their gains stem from true multimodal understanding or increased model complexity. This work investigates the role of multimodal item embeddings, emphasizing the semantic informativeness of the representations. Initial experiments reveal that embeddings from standard extractors (e.g., ResNet50, Sentence-Bert) enhance performance, but rely on modality-specific encoders and ad hoc fusion strategies that lack control over cross-modal alignment. To overcome these limitations, we leverage Large Vision-Language Models (LVLMs) to generate multimodal-by-design embeddings via structured prompts. This approach yields semantically aligned representations without requiring any fusion. Experiments across multiple settings show notable performance improvements. Furthermore, LVLMs embeddings offer a distinctive advantage: they can be decoded into structured textual descriptions, enabling direct assessment of their multimodal comprehension. When such descriptions are incorporated as side content into recommender systems, they improve recommendation performance, empirically validating the semantic alignment encoded in LVLMs outputs. Our study highlights the importance of semantically rich representations and positions LVLMs as a compelling foundation to build robust and meaningful multimodal representations in recommendation tasks.
The Landscape of Foundation Models for Molecular Chemistry
Authors: Mateusz Praski
Venue: CIKM
Year: 2025

Pre-trained neural networks have recently emerged as powerful tools for molecular data mining, offering an alternative to classical approaches. However, these models are often evaluated on limited datasets with narrow baselines, leaving their benefits unclear. We present the first large-scale benchmark comparing pre-trained molecular embedding models across 20 public datasets spanning classification and regression tasks. Our evaluation covers text-based, graph-based, and multimodal architectures, all tested under a unified methodology. The results show that the classical fingerprint-based models remain highly competitive. Only a few models consistently exceeded the baseline. We also highlight key factors influencing model performance, offering practical guidance for model selection and future improvements in molecular embeddings.
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
Authors: Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
Venue: CIKM
Year: 2025

Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs. However, even advanced LLMs cannot follow complex instructions well, thus limiting the quality of generated data. In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. Specifically, we take the high-quality instruction-response pairs in existing datasets and only adopt advanced LLMs to add complex constraints already met by the responses to the instructions, which naturally reduces costs and data noise. In the experiments, we adopt Llama3-70B-Instruct to back-translate constraints and create a high-quality complex instruction-response dataset, named Crab. We present that post-training on Crab improves multiple backbone LLMs' complex instruction-following ability, evaluated on extensive instruction-following benchmarks. We further find that constraint back-translation also serves as a useful auxiliary training objective in post-training. Our code, data, and models are released to facilitate future research.
LHMformer: Long-Range Historical Memory-Enhanced Transformer for Traffic Forecasting
Authors: Guoping Qi, Jihong Liu
Venue: CIKM
Year: 2025

As a typical task of multivariate time series, traffic forecasting has always held significant application value across various fields. Most existing models are constrained by fixed input lengths, making it difficult to avoid the ambiguity in spatio-temporal features. We revisited the synergistic effects of Transformer-based models and TCN-MLP models in spatio-temporal data forecasting and proposed the Long-Range Historical Memory-Enhanced transformer (LHMformer). The network consists of a transformer module for processing short-term inputs and a historical feature extraction module for processing long-term inputs. Experiments on six real-world traffic datasets show that the proposed method achieves state-of-the-art results. Follow-up experiments demonstrate that the historical feature extraction module is a key component in solving traffic flow forecasting problems.
Personalized Federated Recommendation with Multi-Faceted User Representation and Global Consistent Prototype
Authors: Jiaming Qian, Xinting Liao, Xiangmou Qu, Zhihui Fu, Xingyu Lou, Changwang Zhang, Pengyang Zhou, Zijun Zhou, Jun Wang, Chaochao Chen
Venue: CIKM
Year: 2025

Personalized recommender systems are critical for enhancing user engagement across a range of digital platforms. However, conventional approaches rely heavily on centralized data collection, raising significant privacy concerns. Federated recommender systems (PFRS) address these concerns by decentralizing model training, ensuring user data privacy. Despite the progress, existing methods still struggle with capturing the multi-faceted nature of user and transferring global knowledge effectively. In this work, we propose FedMUR, a novel federated recommendation framework that models user representation as a Gaussian mixture distribution, capturing users' multi-faceted characteristics. Each Gaussian component corresponds to a distinct interest facet, with adaptive mixture weights representing the user's preference intensity toward each facet. To facilitate knowledge transfer, FedMUR constructs global consistent prototypes that encode shared behavioral trends across users via popularity-weighted optimal transport. These prototypes enhance local models by injecting global shared patterns into personalized representation learning. Extensive experiments across several real-world datasets demonstrate that FedMUR significantly outperforms existing state-of-the-art federated recommendation systems.
TCFMamba: Trajectory Collaborative Filtering Mamba for Debiased Point-of-Interest Recommendation
Authors: Jin Qian, Shiyu Song, Xin Zhang, Dongjing Wang, He Weng, Haiping Zhang, Dongjin Yu
Venue: CIKM
Year: 2025

Next Point-of-Interest (POI) recommendation, which predicts users' future destinations based on their potential interests, has emerged as a critical task in location-based social networks (LBSNs). However, this task remains challenged by issues such as popularity bias, exposure bias, and limited representational capacity, all of which impede the accurate modeling of users and POIs, thereby restricting balanced and effective recommendations. Therefore, we propose Trajectory Collaborative Filtering Mamba (TCFMamba), which integrates two specially designed modules, i.e., Joint Learning of Static and Dynamic Representations (JLSDR) and Preference State Mamba Network (PSMN), for debiased Point-of-Interest recommendation.
Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten
Authors: Wei Qian, Chenxu Zhao, Yangyi Li, Wenqian Ye, Mengdi Huai
Venue: CIKM
Year: 2025

Currently, various uncertainty quantification methods have been proposed to provide certainty and probability estimates for deep learning models' label predictions. Meanwhile, with the growing demand for the right to be forgotten, machine unlearning has been extensively studied as a means to remove the impact of requested sensitive data from a pre-trained model without retraining the model from scratch. However, the vulnerabilities of such generated predictive uncertainties with regard to dedicated malicious unlearning attacks remain unexplored. To bridge this gap, for the first time, we propose a new class of malicious unlearning attacks against predictive uncertainties, where the adversary aims to cause the desired manipulations of specific predictive uncertainty results. We also design novel optimization frameworks for our attacks and conduct extensive experiments, including black-box scenarios. Notably, our extensive experiments show that our attacks are more effective in manipulating predictive uncertainties than traditional attacks that focus on label misclassifications, and existing defenses against conventional attacks are ineffective against our attacks.
Invariant Treatment Effect Estimation via Consistent Constraints and Information Bottleneck
Authors: Siwei Qiang
Venue: CIKM
Year: 2025

Considerable research has focused on the challenge of estimating individual treatment effects (ITE) from observational data, primarily due to the presence of treatment assignment bias. To address this, practitioners often adjust for relevant covariates to correct potential biases. However, indiscriminately adjusting for all observed covariates risks including 'bad controls'-variables that can introduce bias when conditioned upon, thereby compromising ITE estimation accuracy. To tackle this issue, we propose Invariant Treatment Effect Estimation via Consistent Constraints and Information Bottleneck (CIBITE). This method mitigates the impact of bad controls by leveraging diverse environments and adjusting for confounding factors in observational data, enabling robust ITE estimation. We introduce a novel invariant causal prediction framework to eliminate bad controls while retaining sufficient information for confounding adjustment. This is achieved by imposing consistent constraints on both the representation and output layers of the neural network. Additionally, the Information Bottleneck is employed to reduce the influence of pseudo-invariant features. To further address confounding, we propose a balanced representation learning framework using adversarial training. Extensive experiments on synthetic, semi-simulated, and real-world datasets demonstrate the effectiveness of our approach. The proposed method significantly outperforms state-of-the-art ITE estimation techniques and existing Invariant Risk Minimization (IRM)-based methods.
GLCN: Treatment Effect Estimation via Global-Local Networks with Adversarial Debiasing
Authors: Siwei Qiang
Venue: CIKM
Year: 2025

Estimating Individual Treatment Effects (ITE) from observational data has been widely applied across various domains. The challenge lies in the fact that observational data only includes outcomes under one treatment, while potential outcomes under different treatments need to be inferred. We propose GLCN, a novel causal effect estimation neural network that fuses global-local modeling. The Global branch, trained on the entire dataset, captures overall causal relationships to ensure estimation stability, while the Local branch adopts a matching approach to borrow outcomes from neighboring instances, thereby capturing local heterogeneity. A gating network dynamically integrates predictions from both branches through an adaptive weighting mechanism to enhance adaptability to local variations. A key difficulty in the Local branch lies in defining a reasonable distance metric for neighboring instances. To address this, our method employs a match-and-reconstruct strategy, where the reconstruction error serves as a supervisory signal to guide learning. To mitigate confounding bias, we introduce a confusion loss based on adversarial training. Extensive experiments on public benchmarks and real-world industrial datasets demonstrate that our method outperforms state-of-the-art approaches.