OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Denoising Implicit Feedback for Robust Recommender System
Authors: Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, Xueqi Cheng
Venue: WWW
Year: 2025

While implicit feedback is foundational to modern recommender systems, factors such as human error, uncertainty, and ambiguity in user behavior inevitably introduce significant noise into this feedback, adversely affecting the accuracy and robustness of recommendations. To address this issue, existing methods typically aim to reduce the training weight of noisy feedback or discard it entirely, based on the observation that noisy interactions often exhibit higher losses in the overall loss distribution. However, we identify two key issues: (1) there is a significant overlap between normal and noisy interactions in the overall loss distribution, and (2) this overlap becomes even more pronounced when transitioning from pointwise loss functions (e.g., BCE loss) to pairwise loss functions (e.g., BPR loss). This overlap leads traditional methods to misclassify noisy interactions as normal, and vice versa. To tackle these challenges, we further investigate the loss overlap and find that for a given user, there is a clear distinction between normal and noisy interactions in the user's personal loss distribution. Based on this insight, we propose a resampling strategy to Denoise using the user's Personal Loss distribution, named PLD, which reduces the probability of noisy interactions being optimized. Specifically, during each optimization iteration, we create a candidate item pool for each user and resample the items from this pool based on the user's personal loss distribution, prioritizing normal interactions. Additionally, we conduct a theoretical analysis to validate PLD's effectiveness and suggest ways to further enhance its performance. Extensive experiments conducted on three datasets with varying noise ratios demonstrate PLD's efficacy and robustness.
Triangle Matters! TopDyG: Topology-aware Transformer for Link Prediction on Dynamic Graphs
Authors: Xin Zhang, Fei Cai, Jianming Zheng, Zhiqiang Pan, Wanyu Chen, Honghui Chen, Chonghao Chen
Venue: WWW
Year: 2025

Dynamic graph link prediction is widely utilized in the complex web of the real world, such as social networks, citation networks, recommendation systems, etc. Recent Transformer-based link prediction methods on dynamic graphs not only fail to model the fine-grained structures such as triangles with the vanilla Transformers in the graph serialization process, but also amplify the imbalanced distribution of graphs because of their over-estimation of high-degree nodes. To tackle these issues, we propose a Topology-aware Transformer on Dynamic Graph (TopDyG) for link prediction, consisting of a topology injected Transformer (Ti-Transformer) and a mutual information learning (Mi-Learning). The Ti-Transformer explores the explicit structure of serialized graphs, capturing the topological features. The Mi-Learning mines the relationship between nodes by modeling the mutual information with a prior knowledge, alleviating the over-estimation of high-degree nodes when applying the Transformer-based models for the dynamic graph link prediction task. Extensive experiments on four public datasets containing both transductive and inductive settings present the superiority of our proposal. In particular, TopDyG presents an improvement of 43.27% and 28.75% against the state-of-the-art baselines in terms of NDCG and Jaccard, respectively. The advantages are especially obvious on the high-density graphs.
Collaborative Optimization Approach for Workflow Agents in User Behavior Modeling
Authors: Xinyu Zhang, Ran Dou, Enrui Hu, Minjun Zhao, Yangkai Ding, Zhicheng Dou
Venue: WWW
Year: 2025

User behavior modeling is increasingly critical for personalized services and decision-making systems, yet integrating diverse user and product features into a coherent review generation process remains challenging. Thus we propose a novel collaborative optimization approach for workflow agents in user behavior modeling that integrates user and product feature extraction with review and rating generation. Firstly, to solve the difficulty in determining the optimal structure of agent workflows, we employ Monte Carlo Tree Search (MCTS) to optimize the workflow architecture, establishing a high-performance baseline. Meanwhile, to tackle the challenges in generating and optimizing single-agent prompts and demonstrations, we implement a heuristic optimization strategy for joint automated tuning of system prompts and demo cases. Furthermore, through comprehensive analysis of data distributions, we construct a dynamic routing mechanism for the agent workflow, achieving enhanced performance across diverse scenario-specific datasets. We validate the effectiveness of our methods on three real-world datasets, demonstrating significant performance improvements across all proposed techniques. This approach secured second place overall (and first in star-rating prediction) in the user modeling track of the AgentSociety Challenge @ WWW 2025.
Resource-Efficient Learning for the Web
Authors: Chuxu Zhang, Kaize Ding, Jundong Li, Dongkuan Xu, Haoyu Wang, Derek Zhiyuan Cheng, Huan Liu
Venue: WWW
Year: 2025

Deep learning techniques have demonstrated impressive effectiveness across a wide array of web applications. Notably, graph neural networks (GNNs) and large language models (LLMs) have become essential tools for modeling the extensive graph-structured data and text/language data that populate the web. Despite their success, the advancement of these methods is frequently hampered by resource constraints. Key challenges include the scarcity of labeled data (data-level constraints) and the demand for smaller model sizes suitable for real-world computing environments (model-level constraints). Addressing these issues is crucial for the effective and efficient deployment of models across various real-world web systems and applications, such as social networks, search engines, recommender systems, question answering, and content analysis. Therefore, there is an urgent need to develop innovative and efficient learning techniques that can overcome these resource limitations from both data and model perspectives. In this lecture-style tutorial, we will focus on state-of-the-art approaches in resource-efficient learning, specifically exploring a range of data- and model-efficient methods for GNNs and LLMs, along with their practical applications in web contexts. Our objectives for this tutorial are threefold: (1)to categorize challenges in resource-efficient learning and discuss data and model constraints; (2) to provide a comprehensive review of existing methods and recent advances in resource-efficient learning, particularly concerning GNNs and LLMs; and (3) to highlight open questions and potential future research directions in this rapidly evolving field. Together, these objectives will provide participants with a comprehensive understanding of resource-efficient learning for GNNs and LLMs, its challenges, and its potential for future advancements.
RelWeb 2025: The International Workshop on Resource-Efficient Learning for the Web
Authors: Chuxu Zhang, Kaize Ding, Jundong Li, Dongkuan Xu, Haoyu Wang, Derek Zhiyuan Cheng, Huan Liu
Venue: WWW
Year: 2025

Deep learning techniques, such as large language models and graph neural networks, have demonstrated impressive effectiveness across various web applications. Despite their success, the advancement of these methods is frequently hampered by different resource constraint challenges. Key challenges include the scarcity of labeled data (data-level constraints), the need for smaller model sizes that fit real-world computing environments (model-level constraints), and the integration of neural network design with system and hardware for energy efficiency (system-level constraints). Tackling these issues is essential for the effective and efficient deployment of models in various real-world web systems and applications, including social networks, search engines, recommender systems, question answering, and content analysis. Therefore, there is an urgent need to develop innovative and efficient learning techniques that can overcome these resource limitations. The proposed international workshop on ''Resource-Efficient Learning for the Web (RelWeb 2025)'' will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities for resource-efficient learning. Our workshop objectives are threefold: (1) to establish an engaging platform where experts and participants can share their latest research findings and innovative practices in resource-efficient learning; (2) to explore emerging technologies and trends that could shape the future of this field; and (3) to foster a collaborative environment that encourages partnerships and exchanges among participants. Together, we can advance resource-efficient learning and propel future research in this area.
Explainable and Efficient Editing for Large Language Models
Authors: Tianyu Zhang, Junfeng Fang, Houcheng Jiang, Baolong Bi, Xiang Wang, Xiangnan He
Venue: WWW
Year: 2025

Large Language Models (LLMs) exhibit remarkable capabilities in storing and retrieving vast amounts of factual knowledge. However, they retain outdated or incorrect information from Web corpora. Since full retraining is costly, locate-and-edit model editing methods offer a feasible alternative. Current methods typically follow a two-stage paradigm: (1) identifying critical layers that store knowledge and (2) updating their parameters to store new knowledge. However, both phases have their inherent limitations. Firstly, layer identification is independent of the knowledge being updated, ignoring the differences in knowledge storage patterns. Secondly, parameter updating suffers from high computational overhead due to gradient descent. To solve these, we propose an Explainable and effiCient model Editing method, termed ECE. Specifically, we integrate LLM explainability into the editing process, enabling the adaptive identification of the crucial neurons. Through clustering similar knowledge, we enable batch optimization in a single gradient step, significantly reducing computational time without compromising effectiveness. Extensive experiments demonstrate that ECE can achieve superior performance, showcasing the potential of explainability-driven editing methods for LLMs. Code is available at https://github.com/tianyuzhangterry/ECE.
Intelligent Interaction Platform for Personalized Digital Tutor: Advancing Empathetic and Adaptive Learning Experiences
Authors: Kai Zhang, Weiyin Gong, Qi Liu, Junyu Lu, Meikai Bao, Xukai Liu, Linbo Zhu, Enhong Chen
Venue: WWW
Year: 2025

The rapid advancement of artificial intelligence and virtual digital human technologies is ushering in transformative changes in education. Virtual digital humans, empowered by multimodal information fusion and natural language processing capabilities, offer a more engaging and humanized interaction experience for online learning. However, general-purpose AI models for digital teaching often face notable challenges: 1) They often lack deep understanding of users, overlooking personalized learning needs; 2) they may struggle with knowledge reasoning, leading to unreliable and outdated content; 3) their human-machine interactions can feel unnatural, lacking emotional resonance and empathetic support. To address these challenges, we developed an interaction platform focused on delivering personalized digital human services. This platform optimizes learner profiling, domain-specific data integration, and service frameworks to enhance the intelligence, personalization, and quality of digital teaching agents, effectively tackling issues related to shallow user understanding and unreliable reasoning. Through the integration of realistic digital human avatars and personalized interactive technology, the platform enables more natural and seamless human-machine interactions. Additionally, emotional speech synthesis and synchronized facial expression technology allow virtual teachers' facial expressions to dynamically align with their speech, enhancing expressiveness, vitality, and empathy, which together heighten users' sense of immersion and interaction quality. This development marks a significant step toward smarter, more personalized education.
IntervEEG-LLM: Exploring EEG-Based Multimodal Data for Customized Mental Health Interventions
Authors: Shuning Zhang, Yongquan &apos, Owen&apos, Hu, Xin Yi, Suranga Nanayakkara, Xiaoming Chen
Venue: WWW
Year: 2025

Incorporating multimodal physiological signals into mental health intervention may greatly enhance its effectiveness due to the rich semantic information they contain. EEG signals are particularly common and valuable for capturing brain activity, which can provide objective insights into users' mental states. However how to interpret and leverage these signals remains under-explored. This paper initiated the first attempt by proposing IntervEEG-LLM, an end-to-end framework leveraging EEG-based multimodal signals for mental health intervention. IntervEEG-LLM first used LLMs for heterogeneous multimodal physiological understanding of signals and predicted the intermediate modeling results of users. Then it leveraged effective intervention strategies to interpret and apply the modeling results, offering concrete intervention text through natural language interfaces. Our unified framework integrates three EEG-based physiological data modalities and five mental health strategies, offering a promising approach to signal-based mental health intervention.
GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
Authors: Heming Zhang, Di Huang, Yixin Chen, Fuhai Li
Venue: WWW
Year: 2025

The integration of multi-omic data is pivotal for understanding complex diseases, but its high dimensionality and noise present significant challenges. Graph Neural Networks (GNNs) offer a robust framework for analyzing large-scale signaling pathways and protein-protein interaction networks, yet they face limitations in expressivity when capturing intricate biological relationships. To address this, we propose Graph Sequence Language Model (GraphSeqLM), a framework that enhances GNNs with biological sequence embeddings generated by Large Language Models (LLMs). These embeddings encode structural and biological properties of DNA, RNA, and proteins, augmenting GNNs with enriched features for analyzing sample-specific multi-omic data. By integrating topological, sequence-derived, and biological information, GraphSeqLM demonstrates superior predictive accuracy and outperforms existing methods, paving the way for more effective multi-omic data integration in precision medicine https://github.com/FuhaiLiAiLab/GraphSeqLM.
Decentralized and Policy-Aware Serverless Orchestration for the Federated Web
Authors: Yuqiu Zhang, Hans-Arno Jacobsen
Venue: WWW
Year: 2025

As Web services and Web-of-Things (WoT) applications increasingly span multiple administrative domains such as clouds, edges, and community-run platforms, centralized serverless orchestration frameworks struggle to handle heterogeneous policies, data governance constraints, and dynamic workloads. This situation prompts a new design space: federated and decentralized scheduling models for serverless computing that do not rely on a single global coordinator. In this paper, we explore an approach in which each administrative domain autonomously manages its own resources and complies with local regulations, while lightweight decentralized protocols enable these domains to cooperatively share load information, offer resource availability hints, and negotiate function placements. We illustrate how a small set of core mechanisms such as metadata exchange protocols, compliance-driven constraints, and trust-aware placement negotiations, could enable serverless functions to be flexibly placed and migrated across a federated ecosystem. Our experimental analysis suggests that decentralized coordination can maintain service-level objectives (SLOs) under diverse conditions, especially under load spikes, and align better with the policy and compliance requirements that characterize a heterogeneous Web environment. This paper charts a path forward for research on federated serverless orchestration, aiming to foster more flexible, scalable, and policy-aware infrastructure at global scale.
How does Search Affect Personalized Recommendations and User Behavior? Evidence from LLM-based Synthetic Data
Authors: Haoran Zhang, Xin Kang, Junpeng Guo
Venue: WWW
Year: 2025

Search and personalized recommendations represent two prominent Information Retrieval (IR) technologies often used interchangeably by users across online platforms. Recent scholarly and industrial efforts have shifted towards unraveling the intricate interplay between these technologies within user studies and algorithmic frameworks, aiming to foster a deeper understanding of user behaviors and enhance the precision of user modeling. Nevertheless, due to concerns regarding data privacy and the substantial risks associated with data acquisition, the precise mechanisms by which how search influences personalized recommendations and user behavior remain largely unclear. This investigation adopts a Large Language Model (LLM)-based generative agent simulation methodology to produce synthetic user behavioral data, ensuring its refinement and reliability. Through this approach, we examine issues surrounding search utility and delineate the impact of search functionalities on user behavior in the scenario of online retail, while considering both the longitudinal and cross-sectional heterogeneity of these influences. The insights gleaned from this inquiry provide invaluable perspectives for improving user understanding, refining user modeling methodologies, and designing more efficacious algorithms.
How much Medical Knowledge do LLMs have? An Evaluation of Medical Knowledge Coverage for LLMs
Authors: Ziheng Zhang, Zhenxi Lin, Yefeng Zheng, Xian Wu
Venue: WWW
Year: 2025

Previous evaluation frameworks for large language models (LLMs) have mostly relied on existing question-answering benchmarks, which are primarily task-oriented rather than knowledge-oriented. In the medical domain, however, the effective deployment of LLMs necessitates a thorough evaluation of their medical knowledge coverage. To this end, we propose a systematic evaluation framework, MedKGEval, to assess the coverage of medical knowledge in LLMs through the lens of medical knowledge graphs (KGs). MedKGEval transforms various levels of knowledge (entity-level, relation-level, and subgraph-level) from the medical KG into distinct groups of question-answer pairs, which serve as comprehensive evaluation benchmarks. In addition to traditional task-oriented evaluations, MedKGEval introduces a novel knowledge-oriented evaluation approach that encompasses the assessment of knowledge coverage across entities, relations, and triples. This multi-aspect evaluation approach allows for a more nuanced understanding of LLMs' knowledge coverage in the medical context. Using these benchmarks, we conduct a systematic evaluation of 11 LLMs from multiple perspectives, revealing insights into their strengths and weaknesses in medical knowledge memorization and reasoning.
Comment Staytime Prediction with LLM-enhanced Comment Understanding
Authors: Changshuo Zhang, Zihan Lin, Shukai Liu, Yongqi Liu, Han Li
Venue: WWW
Year: 2025

In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section is essential for comprehensive user interest modeling. A key factor of user engagement is staytime, which refers to the amount of time that users browse and post comments. Existing watchtime prediction methods struggle to adapt to staytime prediction, overlooking interactions with individual comments and their interrelation. In this paper, we present a micro-video recommendation dataset with video comments (named as KuaiComt) which is collected from Kuaishou platform. correspondingly, we propose a practical framework for comment staytime prediction with LLM-enhanced Comment Understanding (LCU). Our framework leverages the strong text comprehension capabilities of large language models (LLMs) to understand textual information of comments, while also incorporating fine-grained comment ranking signals as auxiliary tasks. The framework is two-staged: first, the LLM is fine-tuned using domain-specific tasks to bridge the video and the comments; second, we incorporate the LLM outputs into the prediction model and design two comment ranking auxiliary tasks to better understand user preference. Extensive offline experiments demonstrate the effectiveness of our framework, showing significant improvements on the task of comment staytime prediction. Additionally, online A/B testing further validates the practical benefits on industrial scenario. Our dataset KuaiComt https://github.com/lyingCS/KuaiComt.github.io and code for LCU https://github.com/lyingCS/LCU are fully released.
Perceiving Urban Inequality from Imagery Using Visual Language Models with Chain-of-Thought Reasoning
Authors: Yunke Zhang, Ruolong Ma, Xin Zhang, Yong Li
Venue: WWW
Year: 2025

The rapid pace of urbanization has led to unequal benefits for residents, creating significant inequality issues and discussions around Sustainable Development Goals 10 and 11. Accurate measurement of inequality within urban areas is essential for effective mitigation strategies. Traditional methods rely on survey-based census data, which are time-consuming and delayed, while some studies use coarse proxies like nighttime lights. However, these methods are limited by resolution and fail to capture fine-grained disparities within communities. To address this, we aim to leverage accessible urban imagery, which offers detailed visual features. Two key challenges must be addressed: 1) accurately perceiving micro-level inequalities within neighborhoods, and 2) ensuring that this perception is interpretable for policy guidance. To address these gaps, we propose UI-CoT, a framework that leverages the power of urban imagery-based visual language models in urban inequality perceiving, enhanced by Chain-of-Thought prompting to improve reasoning capabilities. We fine-tune a visual language model to predict three essential neighborhood inequality indicators: the income Gini coefficient, dominant race, and racial income ratio. Extensive experiments show that our model can effectively perceive micro-level inequalities, with the incorporation of Chain-of-Thought reasoning further improving the model's performance by 17.2%. This research offers valuable insights into addressing inequalities within urban environments and demonstrates the potential of web resources in empowering urban sustainable development. The code and data are available at https://github.com/tsinghua-fib-lab/UI-CoT.