OpenRaiser/Intern-Atlas huggingface dataset:
LLMCE: Adapting LLMs with Adversarial Debiasing for Counterfactual Estimation over Time
Authors: Siwei Qiang
Venue: CIKM
Year: 2025

Causal inference in time series data is a challenging yet crucial task in real-world applications such as healthcare and economics, where time-varying confounders often complicate causal effect estimation. In this work, we propose LLMCE, a novel approach that leverages frozen LLMs for counterfactual estimation in time series settings. First, we adapt LLMs to time series by encoding time-varying covariates, past outcomes, and past and future treatments using a reprogramming mechanism. This aligns time series with textual modalities, enabling LLMs to generalize efficiently for time series analysis. Second, to address time-varying confounders, we introduce an adversarial debiasing strategy. This ensures that learned representations predict outcomes without incorporating incremental information about future treatments beyond what can be inferred from past treatments. This dual objective enhances causal estimate reliability while maintaining predictive accuracy. We evaluate LLMCE on synthetic and real-world datasets, demonstrating superior performance compared to state-of-the-art baselines.
Thematic Bottleneck Models for Multimodal Analysis of School Attendance
Authors: Tingrui Qiao, Caroline Walker, Chris Cunningham, Adam Jang-Jones, Susan M. B. Morton, Kane Meissel, Yun Sing Koh
Venue: CIKM
Year: 2025

Regular school attendance is critical for young people, supporting academic achievement, social development, and the cultivation of lifelong habits. Existing research for analysing attendance patterns often relies on structured survey data targeted at their parents and teachers, which overlooks students' perspectives and experiences. To address this gap, our team developed and deployed the Our Journey platform, which enables young people to share their experiences through multimodal responses such as texts and images, offering unique insights into the factors influencing school attendance. The data is linked to official attendance records from the Ministry of Education, allowing the modelling of attendance outcomes based on students' input. To effectively analyse the data, we propose Thematic Bottleneck Models (TBMs) to enhance the understanding of subjective experiences behind data and the interpretability of attendance modelling. TBMs introduce qualitative concepts as intermediate labels, mapping multimodal data to qualitative insights from thematic analysis before the outcomes. The attendance modelling with TBMs outperforms existing multimodal methods in predicting attendance percentage and persistent absenteeism. Analysis of themes within TBMs reveals motivational and contextual factors associated with regular attendance and persistent absenteeism. The findings are used to inform education policy and guide strategies to support student engagement in New Zealand.
Spatial Semantic-based Enhanced Address Parsing via Adaptive Weighted Learning
Authors: Huiling Qin, Ming Wang, Yuanxun Li, Junbo Zhang, Yu Zheng
Venue: CIKM
Year: 2025

Address parsing is an essential task that transforms natural language descriptions into standardized addresses, crucial for numerous urban applications. Existing methods struggle with ambiguous expressions, and even Large Language Models face challenges adapting to specialized domains with limited data. In this study, we focus on developing a robust framework to map diverse address descriptions into a unified semantic space of standardized addresses. We propose the Adaptive Weighted Learning-based Address Parsing (AWLAP) framework, which enhances parsing effectiveness through two key components: a multi-level constrained classifier that mines correlations between geographic entities across hierarchies, and an integrated discriminator that adaptively guides optimization based on parsing complexity. We evaluate the AWLAP using real data from JD Logistics and Point-of-Interest addresses. Extensive experiments comparing against state-of-the-art methods demonstrate AWLAP's effectiveness and robustness in address parsing. The proposed AWLAP framework has been successfully deployed as an address parsing service in practical applications.
Land Deformation Prediction via Multi-modal Adaptive Association Learning
Authors: Wanghui Qiu, Shiyan Hu, Chenjuan Guo, Wenbing Shi, Lina Yu, Ming Gao, Aoying Zhou, Bin Yang
Venue: CIKM
Year: 2025

Accurate land deformation prediction using InSAR (Interferometric Synthetic Aperture Radar) technology is crucial for early warning of geological disasters. However, existing prediction methods face two major challenges: cross-area association bottleneck and inadequate handling of temporal distribution heterogeneity. To address these challenges, we propose Multi-modal Adaptive Association Learning framework (MAAL). For the spatial knowledge transfer challenge, we introduce a cross-area multi-modal association learning module that integrates multi-modal (InSAR and geological text) data to enable knowledge transfer between areas with similar geological characteristics. For temporal distribution heterogeneity, we develop an adaptive evolution stage recognition module that uses distribution routers to identify different temporal patterns, then applies corresponding linear extractors to model the heterogeneous landslide evolution. Experimental validation on 889 hazardous areas demonstrates that MAAL outperforms baselines.
Controlled Feature Interaction Selection for Deep Sparse Networks
Authors: Yuhang Qiu, Biqin Song, Hong Chen
Venue: CIKM
Year: 2025

Deep sparse networks (DSNs) have demonstrated exceptional performance for nonlinear estimation and feature selection, which is crucial for enhancing predictive performance and interpretability. However, existing methods often overlook feature interactions and lack theoretical guarantees on false discovery rate (FDR), especially under interaction scenarios.To address the above issues, this paper develops a DSN-based knockoffs inference framework for feature interaction selection. Theoretical guarantee can be provided by knockoffs inference for controlling on FDR. Empirical evaluations on synthetic datasets demonstrate the capabilities of our proposal on FDR control and identification of informative interactions.
UniROM: Unifying Online Advertising Ranking as One Model
Authors: Junyan Qiu, Ze Wang, Fan Zhang, Zuowu Zheng, Jile Zhu, Jiangke Fan, Teng Zhang, Haitao Wang, Xingxing Wang
Venue: CIKM
Year: 2025

The Multi-stage Cascading Architecture (MCA), widely adopted in industrial advertising systems to balance efficiency and effectiveness, suffers from critical limitations: 1) ranking inconsistency caused by conflicting modeling objectives and capacity gaps across stages, and 2) the inability to model externalities-mutual influences among candidate ads in ranking stages. These issues degrade system performance and lead to suboptimal platform revenue. In this paper, we present UniROM, an end-to-end generative architecture that Unifies online advertising Ranking as One Model. UniROM replaces cascaded stages with a single model to directly generate optimal ad sequences from the full candidate ad corpus in location-based services (LBS). The primary challenges associated with this approach stem from high costs of feature processing and computational bottlenecks in modeling externalities of large-scale candidate pools. To address these challenges, UniROM introduces an algorithm and engine co-designed hybrid feature service to decouple user and ad feature processing, reducing latency while preserving expressiveness. To efficiently extract intra- and cross-sequence mutual information, we propose RecFormer with an innovative cluster-attention mechanism as its core architectural component. Furthermore, we propose a bi-stage training strategy that integrates pre-training with reinforcement learning-based post-training to meet sophisticated platform and advertising objectives. Extensive offline evaluations on public benchmarks and large-scale online A/B testing on industrial advertising platform have demonstrated the superior performance of UniROM over state-of-the-art MCAs.
OBDD-NET: End-to-End Learning of Ordered Binary Decision Diagrams
Authors: Junming Qiu, Rongzhen Ye, Weilin Luo, Kunxun Qi, Hai Wan, Yue Yu
Venue: CIKM
Year: 2025

Learning Ordered Binary Decision Diagrams (OBDDs) from large-scale datasets is an important topic of explainable artificial intelligence. However, existing search-based methods are still limited in scalability regarding dataset size, since they must explicitly encode the satisfaction of all examples in a dataset. To tackle this challenge, we introduce an OBDD encoding method to parameterize a neural network. This method frees satisfaction encoding of all examples in a dataset while leveraging mini-batch training techniques to enhance learning efficiency. Our main theoretical contribution is to prove that our approach enables the simulation of OBDD inference within a continuous space. Besides, we identify faithful OBDD encoding to fulfill the properties required by OBDDs, allowing to interpret an OBDD directly from the learned parameter assignment. With faithful OBDD encoding, we present an end-to-end neural model named ØBDDNet, being capable of coping with large-scale datasets. Experimental results exhibit better scalability and competitive prediction performance of ØBDDNet compared to state-of-the-art OBDD learners. Valuable insights about faithful OBDD encoding are derived from the ablation study. The implementation is available at: https://github.com/jmq-design/OBDD-NET.
GRLND: A Graph Reinforcement Learning Framework for Network Dismantling
Authors: Hongbo Qu, Xu Wang, Yurong Song, Wei Ni, Guo-Ping Jiang, Quan Z. Sheng
Venue: CIKM
Year: 2025

Network Dismantling (ND) seeks to identify the smallest subset of nodes whose removal fragments a network into disconnected components. Traditional methods rely on fixed centrality heuristics or supervised models trained on synthetic data, often failing to generalize across diverse topologies. We introduce GRLND, a Graph Reinforcement Learning framework that enables fully unsupervised, structure-aware dismantling through end-to-end optimization. GRLND formulates ND as a single-step Markov Decision Process (MDP), where the action is a binary mask indicating the nodes to be removed-allowing the agent to generate a complete dismantling strategy in a single forward pass while accounting for the joint effect of multiple node removals. The framework combines a Graph Convolutional Network (GCN) for topological encoding with a stochastic policy trained via the REINFORCE algorithm. Additionally, we design a task-specific reward that balances connectivity disruption and removal sparsity, guiding the policy toward compact yet high-impact dismantling solutions. Experiments on both synthetic and real-world networks show that GRLND consistently outperforms classical heuristics and recent learning-based methods, achieving strong generalization without requiring labels or pretraining.
Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts
Authors: Yunke Qu, Liang Qu, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin
Venue: CIKM
Year: 2025

Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing users' latest preferences is challenging, as interactions reflecting recent interests are limited and new items often lack sufficient feedback. A common solution is to enrich item representations using multimodal encoders (e.g., BERT or ViT) to extract visual and textual features. However, these encoders are pretrained on general-purpose tasks: they are not tailored to user preference modeling, and they overlook the fact that user tastes toward modality-specific features such as visual styles and textual tones can also drift over time. This presents two key challenges in streaming scenarios: the high cost of fine-tuning large multimodal encoders, and the risk of forgetting long-term user preferences due to continuous model updates. To tackle these challenges, we propose Expandable Side Mixture-of-Experts (XSMoE), a memory-efficient framework for multimodal streaming recommendation. XSMoE attaches lightweight side-tuning modules consisting of expandable expert networks to frozen pretrained encoders and incrementally expands them in response to evolving user feedback. A gating router dynamically combines expert and backbone outputs, while a utilization-based pruning strategy maintains model compactness. By learning new patterns through expandable experts without overwriting previously acquired knowledge, XSMoE effectively captures both cold start and shifting preferences in multimodal features. Experiments on three real-world datasets demonstrate that XSMoE outperforms state-of-the-art baselines in both recommendation quality and computational efficiency.
ThoughtForest-KGQA: A Multi-Chain Tree Search for Knowledge Graph Reasoning
Authors: Xingrun Quan, Yongkang Zhou, Junjie Yao
Venue: CIKM
Year: 2025

Most multi-hop Knowledge Graph Question Answering (KGQA) methods utilize fixed pruning strategies that, while efficient, critically impair the diversity of answer paths and fail to discover complex or less common correct answers. To address these limitations, this paper introduces ThoughtForest-KGQA, a novel multi-chain tree search algorithm. The method employs a dual-level reinforcement learning framework where a local-level agent optimizes individual reasoning chains by capturing fine-grained semantic details in the knowledge graph. Concurrently, a global-level agent strategically coordinates the simultaneous exploration of multiple chains. Comprehensive evaluations conducted across two distinct KGQA benchmarks reveal that this approach identifies a broader spectrum of correct answers, setting a new state-of-the-art in the field.
Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation
Authors: Yue Que, Yingyi Zhang, Xiangyu Zhao, Chen Ma
Venue: CIKM
Year: 2025

Graph-based recommender systems leverage neighborhood aggregation to generate node representations, which is highly sensitive to popularity bias, resulting in an echo effect during information propagation. Existing graph-based debiasing solutions refine the aggregation process with attempts such as edge reconstruction or weight adjustment. However, these methods remain inadequate in fully alleviating popularity bias. Specifically, this is because 1) they provide no insights into graph aggregation rationality, thus lacking an optimality guarantee; 2) they fail to well balance the training and debiasing process, which undermines the effectiveness. In this paper, we propose a novel approach to mitigate popularity bias through rational modeling of the graph aggregation process. We reveal that graph aggregation is a special form of backdoor adjustment in causal inference, where the aggregation weight corresponds to the historical interaction likelihood distribution. Based on this insight, we devise an encoder-decoder architecture, namely Causality-aware Graph Aggregation Weight Estimator for Debiasing (CAGED), to approximate the unbiased aggregation weight by optimizing the evidence lower bound of the interaction likelihood. In order to enhance the debiasing effectiveness during early training stages, we further design a momentum update strategy that incrementally refines the aggregation weight matrix. Extensive experiments on three datasets demonstrate that CAGED outperforms existing graph-based debiasing methods. Our implementation is available at https://github.com/QueYork/CAGED.
Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora
Authors: Edward Raff, Ryan R. Curtin, Derek Everett, Robert J. Joyce, James Holt
Venue: CIKM
Year: 2025

A classifier using byte n-grams as features is the only approach we have found fast enough to meet requirements in size (sub 2 MB), speed (multiple GB/s), and latency (sub 10 ms) for deployment in numerous malware detection scenarios. However, we've consistently found that 6-8 grams achieve the best accuracy on our production deployments but have been unable to deploy regularly updated models due to the high cost of finding the top-k most frequent n-grams over terabytes of executable programs. Because the Zipfian distribution well models the distribution of n-grams, we exploit its properties to develop a new top-k n-gram extractor that is up to 35× faster than the previous best alternative. Using our new Zipf-Gramming algorithm, we are able to scale up our production training set and obtain up to 30% improvement in AUC at detecting new malware. We show theoretically and empirically that our approach will select the top-k items with little error and the interplay between theory and engineering required to achieve these results.
Towards Robust Continual Test-Time Adaptation via Neighbor Filtration
Authors: Taki Hasan Rafi, Amit Agarwal, Hitesh Laxmichand Patel, Dong-Kyu Chae
Venue: CIKM
Year: 2025

Test-Time Adaptation (TTA) aims to adapt an unseen target domain utilizing the unlabeled target data using a pre-trained source model. Continual TTA is a more challenging paradigm that deals with non-stationary environments during the test data adaptation. Most existing continual TTA methods are based on pseudo-labeling, but often (1) rely on overconfident pseudo-labels and (2) remain unstable under continual distribution shifts leading to error accumulation and catastrophic forgetting. To tackle these limitations, we propose Neighbor-Filtration based Continual Test-Time Adaptation (NF-CTTA), a reliable and memory-aware adaptation framework that addresses these challenges. NF-CTTA first calibrates pseudo-labels using class-conditional calibration error to correct over/under-confidence of the model. To further ensure reliability, we introduce an OOD Neighbor Filtration technique that selects a subset of high-confidence samples based on entropy and neighbor similarity, ensuring consistency within the semantic neighborhood. Finally, we propose a priority-guided memory buffer that retains the most informative low-entropy samples for replay, mitigating catastrophic forgetting across evolving test distributions. Extensive experiments across multiple domain shift benchmarks demonstrate that NF-CTTA achieves superior performance and stability compared to existing TTA and CTTA methods. The code is available at: https://github.com/takihasan/NF-CTTA.
HuggingGraph: Understanding the Supply Chain of LLM Ecosystem
Authors: Mohammad Shahedur Rahman, Peng Gao, Yuede Ji
Venue: CIKM
Year: 2025

Large language models (LLMs) leverage deep learning architectures to process and predict sequences of words, enabling them to perform a wide range of natural language processing tasks, such as translation, summarization, question answering, and content generation. As existing LLMs are often built from base models or other pre-trained models and use external datasets, they can inevitably inherit vulnerabilities, biases, or malicious components that exist in previous models or datasets. Therefore, it is critical to understand these components' origin and development process to detect potential risks, improve model fairness, and ensure compliance with regulatory frameworks. Motivated by that, this project aims to study such relationships between models and datasets, which are the central parts of the LLM supply chain. First, we design a methodology to systematically collect LLMs' supply chain information. Then, we design a new graph to model the relationships between models and datasets, which is a directed heterogeneous graph, having 402,654 nodes and 462,524 edges. Lastly, we perform different types of analysis and make multiple interesting findings.
Towards Understanding Bias in Synthetic Data for Evaluation
Authors: Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz, Nick Craswell, Bhaskar Mitra
Venue: CIKM
Year: 2025

Test collections are crucial for evaluating Information Retrieval (IR) systems. Creating a diverse set of user queries for these collections can be challenging, and obtaining relevance judgments, which indicate how well retrieved documents match a query, is often costly and resource-intensive. Recently, generating synthetic datasets using Large Language Models (LLMs) has gained attention in various applications. While previous work has used LLMs to generate synthetic queries or documents to improve ranking models, using LLMs to create synthetic test collections is still relatively unexplored. Previous work showed that synthetic test collections have the potential to be used for system evaluation, however, more analysis is needed to validate this claim. In this paper, we thoroughly investigate the reliability of synthetic test collections constructed using LLMs, where LLMs are used to generate synthetic queries, labels, or both. In particular, we examine the potential biases that might occur when such test collections are used for evaluation. We first empirically show the presence of such bias in evaluation results and analyse the effects it might have on system evaluation. We further validate the presence of such bias using a linear mixed-effects model. Our analysis shows that while the effect of bias present in evaluation results obtained using synthetic test collections could be significant, for e.g. computing absolute system performance, its effect may not be as significant in comparing relative system performance. Codes and data are available at: https://github.com/rahmanidashti/BiasSyntheticData
LinkedIn Post Embeddings: Industrial Scale Embedding Generation and Usage across LinkedIn
Authors: Sudarshan Srinivasa Ramanujam, Akanksha Bindal, Yu Jiang, Timothy J. Hazen, David Golland, Fengyu Zhang, Daqi Sun, Wanning Li, Birjodh Singh Tiwana, Siddharth Dangi, Peng Yan
Venue: CIKM
Year: 2025

A post embedding (representation of text in embedding space that effectively captures semantic meaning) is a foundational component of LinkedIn that is consumed by product surfaces in retrieval and ranking (e.g., ranking posts in the feed or video tab). This paper presents the post embeddings used at LinkedIn, where a pre-trained transformer-based large language model (LLM) is taken as input and fine-tuned using multi-task learning across a diverse set of semantic labeling tasks. We observe positive transfer, leading to improved performance across all tasks, compared to training them independently. The generated post embeddings outperform baseline models in zero-shot learning, demonstrating its potential for broader applicability. Furthermore, the generated post embeddings' performance surpasses that of OpenAI's ADA-001 and ADA-002 embeddings on LinkedIn specific datasets and tasks. We also describe the offline evaluation methodology and the deployment to our near-line infrastructure, which makes the post embedding available for use within minutes of post creation for any downstream application. We present how the embeddings were applied in the Feed product surface, in both ranking and retrieval stages, and showcase the real world online impact to demonstrate the superior performance of these embeddings. Finally, we also share the results of applying the embeddings to the retrieval system of our video ranking product surface in LinkedIn. These embeddings have been battle-tested in production at LinkedIn for over two years, consistently powering multiple products.