OpenRaiser/Intern-Atlas huggingface dataset:
Maximum In-Support Return Modeling for Dynamic Recommendation with Language Model Prior
Authors: Xiaocong Chen, Siyu Wang, Lina Yao
Venue: CIKM
Year: 2025

Reinforcement Learning-based recommender systems (RLRS) offer an effective way to handle sequential recommendation tasks but often face difficulties in real-world settings, where user feedback data can be sub-optimal or sparse. In this paper, we introduce MDT4Rec, an offline RLRS framework that builds on the Decision Transformer (DT) to address two major challenges: learning from sub-optimal histories and representing complex user-item interactions. First, MDT4Rec shifts the trajectory stitching procedure from the training phase to action inference, allowing the system to shorten its historical context when necessary and thereby ignore negative or unsuccessful past experiences. Second, MDT4Rec initializes DT with a pre-trained large language model (LLM) for knowledge transfer, replaces linear embedding layers with Multi-Layer Perceptrons (MLPs) for more flexible representations, and employs Low-Rank Adaptation (LoRA) to efficiently fine-tune only a small subset of parameters. We evaluate MDT4Rec on five public datasets and in an online simulation environment, demonstrating that it outperforms existing methods.
Personalized Tree-Based Progressive Regression Model for Watch-Time Prediction in Short Video Recommendation
Authors: Xiaokai Chen, Xiao Lin, Changcheng Li, Peng Jiang
Venue: CIKM
Year: 2025

In online video platforms, accurate watch time prediction has become a fundamental and challenging problem in video recommendation. Previous research has revealed that the accuracy of watch time prediction highly depends on both the transformation of watch-time labels and the decomposition of the estimation process. TPM (Tree based Progressive Regression Model) achieves State-of-the-Art performance with a carefully designed and effective decomposition paradigm. TPM discretizes the watch time into several ordinal intervals and organizes them into a binary decision tree, where each node corresponds to a specific interval. At each non-leaf node, a binary classifier is used to determine the specific interval in which the watch time variable most likely falls, based on the prediction outcome at its parent node. The tree structure is central to TPM, as it defines the decomposition of watch time estimation and how ordinal intervals are discretized. However, TPM uses a predefined full binary tree, which may be sub-optimal for two reasons. First, full binary trees imply equal partitioning of the watch time space, which may fail to capture the complexity of real-world distributions. Second, rather than relying on a fixed global structure, we advocate for a personalized, data-driven tree that can be learned end-to-end. Thus, we propose PTPM to enable highly personalized decomposition of watch estimation with better efficacy and efficiency. Moreover, we show that TPM suffers from selection bias due to conditional modeling and propose a simple solution. We conduct extensive experiments on offline datasets and online environments. Offline results show improved watch time accuracy, and online A/B tests further validate the effectiveness of our framework. PTPM has been fully deployed in core traffic scenarios and now serves over 400 million users daily.
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
Authors: Zhuomin Chen, Dan Li, Jiahui Zhou, Shunyu Wu, Haozheng Ye, Jian Lou, See-Kiong Ng
Venue: CIKM
Year: 2025

Time series (TS) data are ubiquitous across various application areas, rendering time series forecasting (TSF) a fundamental task. With the astounding advances in large language models (LLMs), a variety of methods have been developed to adapt LLMs for time series forecasting. Despite unlocking the potential of LLMs in comprehending TS data, existing methods are inherently constrained by their shallow integration of TS information, wherein LLMs typically access TS representations at shallow layers, primarily at the input layer. This causes the influence of TS representations to progressively fade in deeper layers and eventually leads to ineffective adaptation between textual embeddings and TS representations. In this paper, we propose the Multi-layer Steerable Embedding Fusion (MSEF), a novel framework that enables LLMs to directly access time series patterns at all depths, thereby mitigating the progressive loss of TS information in deeper layers. Specifically, MSEF leverages off-the-shelf time series foundation models to extract semantically rich embeddings, which are fused with intermediate text representations across LLM layers via layer-specific steering vectors. These steering vectors are designed to continuously optimize the alignment between time series and textual modalities and facilitate a layer-specific adaptation mechanism that ensures efficient few-shot learning capabilities. Experimental results on seven benchmarks demonstrate significant performance improvements by MSEF compared with baselines, with an average reduction of 31.8% in terms of MSE. The code is available at https://github.com/One1sAll/MSEF.
See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction
Authors: Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng, Lei Zhang, Sheng Chen, Chenghuan Hou, Han Zhu, Jian Xu, Bo Zheng
Venue: CIKM
Year: 2025

Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-fundamentally determine label generation and model optimization. While many industrial platforms support diverse attribution mechanisms (e.g., First-Click, Last-Click, Linear, and Data-Driven Multi-Touch Attribution), conventional approaches restrict model training to labels from a single production-critical attribution mechanism, discarding complementary signals in alternative attribution perspectives. To address this limitation, we propose a novel Multi-Attribution Learning (MAL) framework for CVR prediction that integrates signals from multiple attribution perspectives to better capture the underlying patterns driving user conversions. Specifically, MAL is a joint learning framework consisting of two core components: the Attribution Knowledge Aggregator (AKA) and the Primary Target Predictor (PTP). AKA is implemented as a multi-task learner that integrates knowledge extracted from diverse attribution labels. PTP, in contrast, focuses on the task of generating well-calibrated conversion probabilities that align with the system-optimized attribution metric (e.g., CVR under the Last-Click attribution), ensuring direct compatibility with industrial deployment requirements. Additionally, we propose CAT, a novel training strategy that leverages the Cartesian product of all attribution label combinations to generate enriched supervision signals. This design substantially enhances the performance of the attribution knowledge aggregator. Empirical evaluations demonstrate the superiority of MAL over single-attribution learning baselines, achieving +0.51% GAUC improvement on offline metrics. Online experiments demonstrate that MAL achieved a +2.6% increase in ROI (Return on Investment).
Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
Authors: Hongyi Chen, Jingtao Ding, Xiaojun Liang, Yong Li, Xiao-Ping Zhang
Venue: CIKM
Year: 2025

Source localization in graph information propagation is essential for mitigating network disruptions, including misinformation spread, cyber threats, and infrastructure failures. Existing deep generative approaches face significant challenges in real-world applications due to limited propagation data availability. We present SIDSL (Structure-prior Informed Diffusion model for Source Localization), a generative diffusion framework that leverages topology-aware priors to enable robust source localization with limited data. SIDSL addresses three key challenges: unknown propagation patterns through structure-based source estimations via graph label propagation, complex topology-propagation relationships via a propagation-enhanced conditional denoiser with GNN-parameterized label propagation module, and class imbalance through structure-prior biased diffusion initialization. By learning pattern-invariant features from synthetic data generated by established propagation models, SIDSL enables effective knowledge transfer to real-world scenarios. Experimental evaluation on four real-world datasets demonstrates superior performance with 7.5-13.3% F1 score improvements over baselines, including over 19% improvement in few-shot and 40% in zero-shot settings, validating the framework's effectiveness for practical source localization. Our code can be found here (https://github.com/tsinghua-fib-lab/SIDSL).
ActiViz: Understanding Sample Selection in Active Learning through Boundary Visualization
Authors: Jie Chen, Honghui Du, Dairui Liu, Siteng Ma, Brian MacNamee, Ruihai Dong
Venue: CIKM
Year: 2025

The performance of Active Learning (AL) methods varies widely, influenced by the query strategy, model, and dataset, with the reasons for variation in performance still unclear and insufficiently studied. However, commonly used metrics like accuracy, precision, and recall provide only limited analytical perspectives. No research has effectively uncovered or explained the reasons behind these performance variations, leaving a gap in understanding of the factors that influence the success or failure of AL methods. To address this issue, we propose a novel method and tool leveraging Voronoi Diagrams to visualize AL processes by illustrating interactions between classification decision boundary changes and queried samples across AL iterations. We perform experiments on synthetic and real-world datasets to validate the effectiveness of our method and analyze various AL query strategies. By visualizing the AL process, we illustrate how different query strategies progressively select samples and influence performance in each iteration. This reveals the potential benefits of adapting query strategies at different learning stages to improve active learning efficiency.
Stamp: Semantic-Aware Sub-trajectory Anomaly Detection with Diffusion Multi-model Pool for Evolving Data Streams
Authors: Biao Chen, Junhua Fang, Pingfu Chao, An Liu, Pengpeng Zhao, Lei Zhao
Venue: CIKM
Year: 2025

Trajectory anomaly detection, as a fundamental operation for moving object pattern discovery, plays an irreplaceable and critical role in spatio-temporal location-based services. Conducting online detection based on the current positions and their contextual semantics can significantly enhance the value of trajectory data. However, existing approaches suffer from two fundamental limitations: 1) treat trajectories as indivisible sequences or apply rigid segmentation strategies, and 2) use of a single detection model that struggles to adapt to concept drift caused by evolving trajectory distributions. Such limitations make it impossible to detect abnormal trajectories in a timely and semantically comprehensive manner. To fill this gap, we propose Stamp, a novel framework for Semantic-aware sub-Trajectory Anomaly detection with a diffusion Multi-model Pool. In particular, Stamp comprises three key innovations: 1. It employs a semantic-driven dynamic segmentation mechanism that identifies natural breakpoints in trajectories based on changes in road semantics, rather than fixed rules. 2. It enhances trajectory representation by embedding road network semantic vectors, capturing both spatial geometry and functional urban characteristics. 3. It employs a pool of diffusion models that dynamically evolves through reliability assessment, similarity measurement, and strategic merging operations, ensuring adaptability to concept drift while leveraging the superior generative capabilities of diffusion models over traditional autoencoders. Experimental results demonstrate that Stamp improves detection efficiency by 35%, AUPR by 5.6%, and F1-score by 2.7% on two large-scale real-world urban trajectory datasets when compared to state-of-the-art methods, demonstrating its effectiveness for real-time anomaly detection in complex urban environments.
Spreader Behavior Forecasting: Intent-aware Neural Processes for Intervening Misinformation
Authors: Haoran Chen, Dongmei Han
Venue: CIKM
Year: 2025

The behavior of spreaders on social media evolves continuously, driven by shifting intentions and interactions with emerging news topics. Traditional approaches have focused on identifying misinformation spreaders, but have often relied on a static ground-truth label, limiting their applicability for implementing time-sensitive platform interventions. In contrast, our work tackles spreader behavior forecasting through an account-level credit score, modeling the temporal evolution of spreader behavior to capture the intent shifts that drive misinformation spreading. To this end, we propose a novel Intent-aware Neural Processes (INP) model, which focuses on tracking the evolving intent of spreaders over time. The model leverages a state transition structure and an intent state thinning algorithm to improve latent representations, enabling more accurate predictions of future spreader behavior. Experimental results on restructured datasets demonstrate the effectiveness of INP in identifying temporal risk regions for proactive misinformation intervention.
NR-GCF: Graph Collaborative Filtering with Improved Noise Resistance
Authors: Yijun Chen, Bohan Li, Yicong Li, Lixiang Song, Haofen Wang, Wenlong Wu, Junnan Zhuo, Hongzhi Yin
Venue: CIKM
Year: 2025

Graph Neural Networks (GNNs) have emerged as the preferred backbone model of recommender systems, credited to their strong capability in capturing the intricate topological relationships within user-item interactions. Nevertheless, a common oversight in existing studies is the presumption of the inherent reliability of these interactions, ignoring the reality that a significant fraction of user-item engagements, such as accidental clicks, are inherently noisy. Extensive studies have revealed that GNN is vulnerable to such noisy edges within the graph-structured data, as those noisy edges can mislead the network into overfitting incorrect patterns of interactions, thereby propagating such incorrect information through the entire interaction network.To address these challenges, in this paper, we propose a novel noise-robust GNN-based training strategy for recommendation, known as Noise-Resistant Graph Collaborative Filtering (NR-GCF). NR-GCF innovatively adopts a two-stage learning paradigm to filter out unreliable interactions, leveraging the memorization effect of GNNs. It further utilizes representation modulation to learn noise-resistant embeddings, enhancing robustness for recommendation tasks.Comprehensive experiments and ablation studies demonstrate the effectiveness and robustness of the proposed NR-GCF. Our implementation has been made available at:https://github.com/1197151063/NRGCF.git
Rethinking Client-oriented Federated Graph Learning
Authors: Zekai Chen, Xunkai Li, Yinlin Zhu, Rong-Hua Li, Guoren Wang
Venue: CIKM
Year: 2025

As a new distributed graph learning paradigm, Federated Graph Learning (FGL) facilitates collaborative model training across local systems while preserving data privacy. We review existing FGL approaches and categorize their optimization mechanisms into: (1) Server-Client (S-C), where clients upload local model parameters for server-side aggregation and global updates; (2) Client-Client (C-C), which allows direct exchange of information between clients and customizing their local training process. We reveal that C-C shows superior potential due to its refined communication structure. However, existing C-C methods broadcast redundant node representations, incurring high communication costs and privacy risks at the node level. To this end, we propose FedC4, which combines graph Condensation with C-C Collaboration optimization. Specifically, FedC4 employs graph condensation technique to refine the knowledge of each client's graph into a few synthetic embeddings instead of transmitting node-level knowledge. Moreover, FedC4 introduces three novel modules that allow the source client to send distinct node representations tailored to the target client's graph properties. Experiments on eight public real-world datasets show that FedC4 outperforms state-of-the-art baselines in both task performance and communication cost. Our code is now available on https://github.com/Ereshkigal1/FedC4.
Advancing Temporal Sensitive Question Answering through Progressive Multi-Step Reflection
Authors: Ziyang Chen, Erxue Min, Xiang Zhao, Yunxin Li, Xin Jia, Jinzhi Liao, Shuaiqiang Wang, Baotian Hu, Dawei Yin
Venue: CIKM
Year: 2025

Retrieval-augmented generation (RAG) has demonstrated strong potential in enhancing large language models (LLMs) for complex, real-world question answering. However, existing RAG frameworks remain inadequate for temporal scenarios, primarily due to their inability to jointly model temporal constraints in both retrieval and reasoning. On the retrieval side, traditional approaches focus on semantic similarity, often returning outdated or temporally misaligned evidence. On the generation side, these systems frequently produce factually incorrect or hallucinated answers when confronted with incomplete or temporally inconsistent information. Motivated by the observed limitations, we propose ChronoReflect+, a temporal logic-aware RAG framework that incorporates hybrid temporal-aware retrieval and progressive multi-step reflection. Our method iteratively refines both retrieval and reasoning, identifying and bridging information gaps as context accumulates. Extensive experiments demonstrate that ChronoReflect+ significantly outperforms state-of-the-art RAG baselines-improving end-to-end accuracy by 15.2%-particularly on questions involving implicit time expressions and multi-hop reasoning.
Multimodal Contrastive Learning with Early Fusion for Robust Medical Signal Representation
Authors: Lei Chen, Kyoungsuk Park, Junetae Kim
Venue: CIKM
Year: 2025

Contrastive learning has achieved remarkable success in the representation learning of physiological signals foundation models. However, current approaches often focus on unimodal settings or treat each modality independently, neglecting the rich synergistic information that can emerge from cross-modal interactions. This leads to suboptimal representations that fail to capture complex interdependencies across modalities. To address this limitation, we propose a multimodal contrastive learning framework that aligns fused representations instead of individual signals. Specifically, we encode ECG Lead II, ECG Lead V, and PPG signals using modality-specific encoders, followed by a fusion block to integrate modality-specific embeddings into a unified representation. By applying contrastive learning to the unified representation, our approach effectively mitigates inter-modal conflicts while capturing complementary cross-modal features that would otherwise be lost in traditional alignment strategies. Experiments on MIMIC-III (internal) and VitalDB (external) datasets demonstrate that our approach outperforms existing baselines in patient attribute prediction tasks, validating its effectiveness in learning comprehensive multimodal representations of physiological signals.
Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity
Authors: Qin Chen, Guojie Song
Venue: CIKM
Year: 2025

Heterogeneous graphs (HGs) are common in real-world scenarios and often exhibit heterophily. However, most existing studies focus on either heterogeneity or heterophily in isolation, overlooking the prevalence of heterophilic HGs in practical applications. Such ignorance leads to their performance degradation. In this work, we first identify two main challenges in modeling heterophily HGs: (1) varying heterophily distributions across hops and meta-paths; (2) the intricate and often heterophily-driven diversity of semantic information across different meta-paths. Then, we propose the Adaptive Heterogeneous Graph Neural Network (AHGNN) to tackle these challenges. AHGNN employs a heterophily-aware convolution that accounts for heterophily distributions specific to both hops and meta-paths. It then integrates messages from diverse semantic spaces using a coarse-to-fine attention mechanism, which filters out noise and emphasizes informative signals. Experiments on seven real-world graphs and twenty baselines demonstrate the superior performance of AHGNN, particularly in high-heterophily situations.
Let Topology Speak: Graph Neural Network with Topology-Aware Augmentation
Authors: Kangzhuo Chen, Xiaoqian Sun, Huawei Shen, Xueqi Cheng
Venue: CIKM
Year: 2025

Company financial risk is widespread, and accurate prediction is critical to avoiding significant losses. Many risky companies often exhibit subtle anomalies, incomplete information, or limited interactions with others; however, the types of their interactions remains diverse and informative. Existing methods like metapath-based Graph Neural Networks effectively leverage node relationships but are constrained by manual biases, noise introduction and high computational complexity. Similarly, Graph Transformers show strong performance but suffer from prohibitively high complexity. To overcome these challenges, we propose the Graph Neural Network with Topology-Aware Augmentation (GTA). GTA adopts a dual augmentation strategy based on topology information, augmenting both topology and attributes. It first performs unification encoding on single node-type heterogeneous graphs, integrating heterogeneous topology into node representations. Expressive topology encoding is performed, followed by dual augmentation based on the learned topology embeddings. Through this approach, GTA achieves effective risk prediction. Extensive experiments on the real-world dataset demonstrate GTA's superior performance compared to state-of-the-art metapath-based and graph transformer-based methods, effectively handling sparse graphs with a single node-type and multiple edge-types. Comprehensive ablation study and visual analysis further validate the discriminative power of topology augmentation in distinguishing risky companies. And our code is publicly available at https://github.com/ckz123/GTA.
Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
Authors: Sirui Chen, Changxin Tian, Binbin Hu, Kunlong Chen, Ziqi Liu, Zhiqiang Zhang, Jun Zhou
Venue: CIKM
Year: 2025

Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and data reliability. To address these limitations, we propose a novel program-assisted synthesis framework that systematically generates a high-quality mathematical corpus with guaranteed diversity, complexity, and correctness. This framework integrates mathematical knowledge systems and domain-specific tools to create executable programs. These programs are then translated into natural language problem-solution pairs and vetted by a bilateral validation mechanism that verifies solution correctness against program outputs and ensures program-problem consistency. We have generated 12.3 million such problem-solving triples. Experiments demonstrate that models fine-tuned on our data significantly improve their inference capabilities, achieving state-of-the-art performance on several benchmark datasets and showcasing the effectiveness of our synthesis approach.