OpenRaiser/Intern-Atlas huggingface dataset:
HRCformer: Hierarchical Recursive Convolution-Transformer with Multi-Scale Adaptive Recalibration for Time Series Forecasting
Authors: Dejiang Zhang, Lianyong Qi, Yuwen Liu, Xucheng Zhou, Jianye Xie, Haolong Xiang, Xiaolong Xu, Xuyun Zhang, Yang Cao, Yang Zhang
Venue: CIKM
Year: 2025

Time series forecasting has significant applications across various domains, including industry, agriculture, and finance. Transformer-based models have shown significant promise in enhancing time series forecasting over the past few years. However, existing methods struggle to simultaneously capture local details and global semantics under single-view architectures. They also find it difficult to dynamically adapt to time-varying and multi-scale temporal patterns while accurately modeling the complex, time-varying relationships between multiple variables. To address these challenges, we propose HRCformer, a novel Transformer-based framework that introduces two key innovations: the Hierarchical Recursive Interaction Convolution (HRIC) and the Triad Adaptive Recalibration Module (TARM). HRIC achieves joint modeling of fine-grained short-term fluctuations and high-order cross-period dependencies in time series by integrating Divide-and-Process Convolution for local processing with Recursive Channel Interaction Convolution for global processing. TARM further enhances dynamic modeling via Dynamic Variance Attention, which amplifies critical temporal deviations through 3D attention, and the Adaptive Multivariate Recalibration, which uses a two-layer fully connected network with nonlinear activation to learn the dynamic relationships between channels, suppresses noise, and emphasizes informative multivariate interactions. Comprehensive experiments conducted on seven real-world datasets highlight the superiority of HRCformer compared to prior state-of-the-art methods.
PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization
Authors: Kepu Zhang, Teng Shi, Weijie Yu, Jun Xu
Venue: CIKM
Year: 2025

Personalized retrieval-augmented generation (RAG) aims to produce user-tailored responses by incorporating retrieved user profiles alongside the input query. Existing methods primarily focus on improving retrieval and rely on large language models (LLMs) to implicitly integrate the retrieved context with the query. However, such models are often sensitive to retrieval quality and may generate responses that are misaligned with user preferences. To address this limitation, we propose PrLM, a reinforcement learning framework that trains LLMs to explicitly reason over retrieved user profiles. Guided by a contrastively trained personalization reward model, PrLM effectively learns from user responses without requiring annotated reasoning paths. Experiments on three personalized text generation datasets show that PrLM outperforms existing methods and remains robust across varying numbers of retrieved profiles and different retrievers.
An Robust Entity Alignment Method based on Knowledge Distillation with Noisy Aligned Pairs
Authors: Yuhong Zhang, Hangchi Song, Xiaolong Zhu, Chenyang Bu, Kui Yu
Venue: CIKM
Year: 2025

Entity alignment (EA) aims to find the same entities in different knowledge graphs. Existing EA methods assume the supervised aligned pairs without noise. In applications, noisy pairs lead to degradation of EA performance. To this end, a robust EA method based on knowledge distillation is proposed for noisy pairs. Firstly, the dual-teacher model with online distillation is designed, in which, noise discriminator is performed to improve the noise resistance of teacher models. Secondly, a student model is offline distilled from the dual-teacher model without using the noisy supervised pairs, further enhancing the robustness of student model. In addition, the entity structure is combined with entity representation for alignment inference to alleviate the bias of entity representation in noisy environment. Extensive experiments demonstrate the effectiveness of the proposed method.
A Hierarchical Structure-Enhanced Personalized Recommendation Model for Traditional Chinese Medicine Formulas Based on KG Diffusion Guidance
Authors: Chaobo Zhang, Long Tan
Venue: CIKM
Year: 2025

Artificial intelligence (AI) technology plays a crucial role in recommending prescriptions for traditional Chinese medicine (TCM). Previous studies have made significant progress by focusing on the symptom-herb relationship in prescriptions. However, several limitations hinder model performance: (i) Insufficient attention to patient-personalized information such as age, BMI, and medical history, which hampers accurate identification of syndrome and reduces efficacy. (ii) The typical long-tailed distribution of herb data introduces training biases and affects generalization ability. (iii) The oversight of the 'monarch, minister, assistant and envoy' compatibility among herbs increases the risk of toxicity or side effects, opposing the 'treatment based on syndrome differentiation' principle in clinical TCM. Therefore, we propose a novel hierarchical structure-enhanced personalized recommendation model for TCM formulas based on knowledge graph (KG) diffusion guidance, namely TCM-HEDPR. Specifically, we pre-train symptom representations using patient-personalized prompt sequences and apply prompt-oriented contrastive learning (CL) for data augmentation. Furthermore, we employ a KG-guided homogeneous graph diffusion method integrated with a self-attention mechanism to globally capture the non-linear symptom-herb relationship. Lastly, we design a heterogeneous graph hierarchical network to integrate herbal dispensing relationships with implicit syndromes, guiding the prescription generation process at a fine-grained level and mitigating the long-tailed herb data distribution problem. Extensive experiments on two public datasets and one clinical dataset demonstrate the effectiveness of TCM-HEDPR. In addition, we incorporate insights from modern medicine and network pharmacology to evaluate the recommended prescriptions comprehensively. It can provide a new paradigm for the recommendation of modern TCM.
Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion
Authors: Moyu Zhang, Yongxiang Tang, Yujun Jin, Jinxin Hu, Yu Zhang
Venue: CIKM
Year: 2025

Traditional recommendation methods model a user's interest in a target item by correlating its embedding with the embeddings of items from the user's interaction history, thereby capturing implicit collaborative filtering signals. Consequently, traditional ID-based methods often encounter data sparsity problems stemming from the sparse nature of ID features. To mitigate this issue, recommendation models incorporate multimodal item information to enhance recommendation accuracy. However, existing multimodal recommendation methods typically rely on early fusion approaches, which focus primarily on combining text and image features, while neglecting the dynamic context provided by user behavior sequences. This oversight precludes the dynamic adaptation of multimodal interest representations to behavioral patterns, thereby hindering the model's ability to effectively capture user multimodal interests. Therefore, this paper proposes the Distribution-Guided Multimodal-Interest Auto-Encoder (DMAE), which achieves the cross fusion of user multimodal interest at the behavioral level. Specifically, DMAE comprises three key components: 1) Multimodal Interest Encoding Unit (MIEU), which encodes the similarity scores between the target item and historically clicked items as the corresponding representation vectors of user interest across different modalities. 2) Multimodal Interest Fusion Unit (MIFU), which dynamically adapts these interest representations through both intra- and inter-modal fusion, a process contextualized by the user's behavioral sequence to achieve a fine-grained and behavior-aware representation of interest. 3) Interest-Distribution Decoding Unit (IDDU), which employs a decoder to reconstruct the encoded user interest representations into true similarity distributions for each modality. The similarity distributions serve as a guide for model learning, aiming to retain as much multimodal information as possible. Ultimately, extensive experiments demonstrate the superiority of DMAE.
VoiceVisSystem: End-to-End Voice-driven Data Visualization Generation from Natural Language Questions
Authors: Haodi Zhang, Xiaohui Tang, Xinhe Zhang, Jihua Zhou, Yuanfeng Song
Venue: CIKM
Year: 2025

In today's digital era, data visualization (DV) technology has become indispensable for tasks involving data processing and graphical reasoning. In this demonstration, we introduce a novel automatic DV system named VoiceVisSystem. VoiceVisSystem is designed for transforming speech-form natural language questions (NLQs) into visual data representations, a task formally known as Speech-to-Vis. Unlike the existing cascaded method (e.g., Sevi), the core component of our system relies on an advanced end-to-end speech-to-vis model named SpeechVisNet, eliminating the need for text as an intermediate medium and directly facilitating the conversion from Speech-form to DV. Specifically, the speech encoder and the text encoder of the SpeechVisNet respectively take the user's NLQs and the corresponding database information as inputs and convert them into hidden representations. Then, a grammar-based decoder generates the corresponding DVs as the output. As a result, our system enjoys the benefits of avoiding error propagation, thereby enhancing accuracy. By offering a seamless solution for the speech-to-vis task, VoiceVisSystem presents a promising tool for practical applications in various domains. The demonstration video is available at https://1drv.ms/v/s!Ah2vhbolPBFMiSNPZLunJ6Qp6jqU?e=Shyq8R.
IPNet: An Interaction Pattern-aware Neural Network for Temporal Link Prediction
Authors: Qingyang Zhang, Yitong Wang, Xinjie Lin
Venue: CIKM
Year: 2025

Temporal link prediction, which aims to predict the future status of edges between target nodes, is vital for current prevalent online services. Most existing methods ignore node-level behavior patterns, which play a decisive role in temporal link prediction, as nodes that behave similarly are more likely to interact in the future. In this paper, we propose a novel continuous-time model, the Interaction Pattern-aware neural Network (IPNet), to capture node-level behavior patterns and network evolution by encoding interaction sequences and contextual windows. We further devise a random walk sampling strategy to enhance the extraction of these windows, preserving node-centric structural evolution. Experimental results on seven real-world networks demonstrate that IPNet outperforms state-of-the-art methods in both transductive and inductive link prediction tasks. The code can be accessed via https://github.com/CoderZQY/IPNet.
Student-Augmented Self-Training with Closed Loop Feedback in Linguistic Steganalysis
Authors: Ziwei Zhang, Juan Wen, Wanli Peng, Haowei Chang
Venue: CIKM
Year: 2025

As a countermeasure to linguistic steganography, linguistic steganalysis aims to distinguish between texts containing hidden secret messages (stego) and natural texts (cover). Current semi-supervised steganalysis approaches rely on a self-training mechanism. However, in linguistic steganalysis, pseudo-label errors propagate through iterative training, causing the student model to reinforce incorrect stego-distribution associations, thereby impairing its discriminative ability for subtle linguistic perturbations. To address this challenge, we propose SALT-LS, a self-training linguistic steganalysis framework that integrates a closed feedback loop between student and teacher models alongside a dual-constraint mechanism to improve pseudo-labels. Unlike a conventional semi-supervised steganalysis approach, we compute prototype penalties from both same-class (rather than intra-class) and cross-class perspectives, enabling more effective use of labeled data. Furthermore, we introduce an advanced-updating strategy for the student model, which is combined with the dual-constraint mechanism, forming a closed feedback loop that continuously refines the teacher model's pseudo-label generation for robust steganalysis performance. Extensive experiments on six datasets, including widely used steganographic strategies and corpora, demonstrate that SALT-LS outperforms state-of-the-art models. Our code is available.
D3-TR: Data-driven Daily Delivery Task Rescheduling for Cost-effective Last-mile Delivery
Authors: Lidi Zhang, Yinfeng Xiang, Wenjun Lyu, Zhiqing Hong, Haotian Wang, Desheng Zhang, Yunhuai Liu, Tian He
Venue: CIKM
Year: 2025

In last-mile logistics, couriers are typically assigned fixed zones to perform door-to-door deliveries. In practice, packages in some delivery zones might not be fulfilled on time due to couriers taking irregular leave for sickness or higher-priority task assignments, e.g., services for VIPs and regulatory training. Beyond the costly real-world practice, i.e., hiring temporary workers, analysis of historical data reveals that a daily delivery task rescheduling among on-duty couriers can be a cost-effective and efficient alternative. It involves individual workload assessments and delivery task assignments, both of which existing methods can not address adequately: (i) Existing courier workload assessment methods are not tailored for downstream optimization tasks, leading to poor performance. (ii) Efficiency-oriented task assignment methods may lead to unfair workload among the couriers. To address the above two limitations, in this paper, we propose D3-TR, a data-driven method for task reassignment among present couriers. Firstly, we design a consistency-guided predictor that can quickly and precisely predict the workload of couriers. Secondly, based on this predictor, we design a workload-aware genetic algorithm to solve the optimal task allocation problem. Experimental results underscore the superiority of our method over several baselines. Furthermore, real-world deployment on millions of orders demonstrates the effectiveness of our solution, yielding an average of 3.9% improvement in the on-time delivery rate.
FEDDGCN: A Frequency-Enhanced Decoupling Dynamic Graph Convolutional Network for Traffic Flow Prediction
Authors: Wendong Zhang, Ruobai Xiang, Zhifang Liao, Peng Lan, Qihao Liang
Venue: CIKM
Year: 2025

As a core task in Intelligent Transportation Systems (ITS), traffic flow prediction is essential for resource allocation and real-time route planning. Effectively capturing complex temporal correlations and dynamic spatial dependencies in traffic flow data is critical yet challenging for accurate prediction. However, existing approaches are still limited by the insufficient capability for spatial-temporal pattern decoupling and the underutilization of frequency domain information. To address these issues, we propose a novel Frequency-Enhanced Dynamic Decoupling Graph Convolutional Network (FEDDGCN), which introduces a gated decoupling mechanism integrating temporal and spatial embeddings to decouple traffic flow into prominent periodic and perturbative component. It also achieves effective pattern separation by incorporating frequency domain analysis with Fourier filters. Furthermore, a dual-branch spatial-temporal learning module, employing a divide-and-conquer strategy, is designed to achieve separate modeling for the two distinct components. Specially, the dynamic graph convolution modules are utilized to learn spatial dependencies and temporal and frequency attention mechanisms further capture complex temporal correlations for prominent periodic and perturbative components.Extensive experiments on multiple real-world datasets demonstrate that FEDDGCN achieves superior predictive performance compared with state-of-the-art methods.
EvalAgent: Towards Evaluating News Recommender Systems with LLM-based Agents
Authors: Guangping Zhang, Peng Zhang, Jiahao Liu, Zhuoheng Li, Dongsheng Li, Hansu Gu, Tun Lu, Ning Gu
Venue: CIKM
Year: 2025

Online news platforms have become the primary source of information consumption, with recommender systems serving as critical gateways that shape public discourse through their algorithmic power, necessitating rigorous evaluation methodologies. Traditional offline evaluation methods struggle with evolving user behavior and dynamic system adaptation, while online experiments are costly, time-consuming, and ethically challenging. To address these challenges, this paper introduces EvalAgent, a large language model agent system for simulating real-world online news recommender systems. EvalAgent employs Stable Memory (StM) to model users' exploration-exploitation dynamics, mitigating noise from irrelevant interactions by analyzing the distribution density of news articles within the short-term memory, and incrementally maintains the long-term memory to capture users' high-level preferences, thereby enabling a consistent and reliable simulation of sustained interactions. It further incorporates an Environment Interaction Framework (EIF) to enable seamless engagement with real-world recommender systems. This approach yields a precise, scalable, and ethically responsible evaluation framework for news recommender systems. Comprehensive experiments and user studies substantiate EvalAgent's efficacy, with publicly available code to support ongoing research in recommender system evaluation.
PMTA: Perception-Aware Multi-Task Transformer Network for Personalized Multi-Domain Adaptation
Authors: Chenbin Zhang, Xiaoxie Zhu, Xingchao Cao, Qiwei Chen, Feng Zhang, Yang Xiao, Zuotao Liu
Venue: CIKM
Year: 2025

The escalating complexity of industrial recommendation systems, characterized by diverse user behaviors and cross-domain application scenarios, necessitates advanced multi-task and multi-domain learning paradigms. Existing methods often struggle with efficient knowledge transfer across tasks and domains due to semantic gaps and distribution shifts. To address these challenges, we propose the Perception-Aware Multi-Task Transformer Network for Personalized Multi-Domain Adaptation (PMTA), a unified framework that integrates three key innovations: First, the Task Prompt Encoding (TPE) module dynamically generates prompts by synthesizing personalized user data with task-specific information. Second, the Transformer-based Multi-Task Perception (TMPN) network enables adaptive cross-task knowledge transfer through attention mechanisms. Third, the Multi-Domain Adaptation (MDAN) component captures domain-specific behavior patterns via learnable prior information. Experimental results demonstrate PMTA's effectiveness, achieving 0.168% increase in watch time and significant improvements in engagement metrics (AAD: +0.0113%, AAH: +0.0608%). Deployed on Douyin and Douyin Lite, it significantly improves recommendation quality and drives commercial success.
FLAIR: Feedback Learning for Adaptive Information Retrieval
Authors: William Zhang, Yiwen Zhu, Yunlei Lu, Mathieu B. Demarne, Wenjing Wang, Kai Deng, Nutan Sahoo, Katherine Lin, Miso Cilimdzic, Subru Krishnan
Venue: CIKM
Year: 2025

Recent advances in Large Language Models (LLMs) have driven the adoption of copilots in complex technical scenarios, underscoring the growing need for specialized information retrieval solutions. In this paper, we introduce FLAIR, a lightweight, feedback learning framework that adapts copilot systems' retrieval strategies by integrating domain-specific expert feedback. FLAIR operates in two stages: an offline phase obtains indicators from (1) user feedback and (2) questions synthesized from documentation, storing these indicators in a decentralized manner. An online phase then employs a two-track ranking mechanism to combine raw similarity scores with the collected indicators. This iterative setup refines retrieval performance for any query. Extensive real-world evaluations of FLAIR demonstrate significant performance gains on both previously seen and unseen queries, surpassing state-of-the-art approaches. The system has been successfully integrated into Copilot DECO, serving thousands of users at Microsoft, demonstrating its scalability and effectiveness in operational environments.
Yes is Harder than No: A Behavioral Study of Framing Effects in Large Language Models Across Downstream Tasks
Authors: Ziheng Zhang, Weixin Zeng, Jiuyang Tang, Ji Wang, Xiang Zhao
Venue: CIKM
Year: 2025

Framing effect is a well-known cognitive bias in which individuals' responses to the same underlying question vary depending on how the question is phrased. Recent studies suggest that large language models (LLMs) also exhibit framing effects, but existing work has primarily replicated psychological experiments using hand-crafted prompts, leaving their impact on practical downstream tasks underexplored. To fill in the gap, in this paper, we conduct a systematic empirical investigation into framing effects in LLMs across multiple real-world downstream tasks. We construct semantically equivalent prompts with positive and negative framings and evaluate a wide range of LLMs under these conditions. We uncover several behavioral regularities of framing effects in LLMs, among which the most notable one is a consistent response asymmetry: LLMs find answering ''yes'' harder than ''no''. That is, LLMs tend to issue affirmative responses (i.e., ''yes'') only when they are highly confident, while they incline to answer negatively (i.e., ''no'') under uncertainty. We interpret this asymmetry through the lens of Error Management Theory (EMT), which posits that rational agents adopt risk-averse strategies to minimize the more costly error. We empirically show that this behavior is partially attributable to a statistical imbalance in the frequency of positive versus negative framing cues in pretraining corpora. Furthermore, we demonstrate that the framing-induced bias in LLMs can inform prompt engineering and active in-context learning, i.e., using framing-sensitive samples as demonstrations can improve model performance. Finally, we offer a preliminary strategy to mitigate the framing effect, i.e., injecting debiasing instructions, which shows promise. In all, our work uncovers a fundamental behavioral bias in LLMs and offers practical guidance for their reliable deployment across downstream tasks.
AppAgent-Pro: A Proactive GUI Agent System for Multidomain Information Integration and User Assistance
Authors: Yuyang Zhao, Wentao Shi, Fuli Feng, Xiangnan He
Venue: CIKM
Year: 2025

Large language model (LLM)-based agents have demonstrated remarkable capabilities in addressing complex tasks, thereby enabling more advanced information retrieval and supporting deeper, more sophisticated human information-seeking behaviors. However, most existing agents operate in a purely reactive manner, responding passively to user instructions, which significantly constrains their effectiveness and efficiency as general-purpose platforms for information acquisition. To overcome this limitation, this paper proposes AppAgent-Pro, a proactive GUI agent system that actively integrates multi-domain information based on user instructions. This approach enables the system to proactively anticipate users' underlying needs and conduct in-depth multi-domain information mining, thereby facilitating the acquisition of more comprehensive and intelligent information. AppAgent-Pro has the potential to fundamentally redefine information acquisition in daily life, leading to a profound impact on human society. Our code is available at: https://github.com/LaoKuiZe/AppAgent-Pro. The demonstration video could be found at: https://www.dropbox.com/scl/fi/hvzqo5vnusg66srydzixo/AppAgent-Pro-demo-video.mp4?rlkey=o2nlfqgq6ihl125mcqg7bpgqu&st=d29vrzii&dl=0.