OpenRaiser/Intern-Atlas huggingface dataset:
MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records
Authors: Ran Xu, Wenqi Shi, Jonathan Wang, Jasmine Zhou, Carl Yang
Venue: WWW
Year: 2025

Efficiently comprehending diagnosis and treatment plans remains a significant challenge for both medical professionals and patients, particularly when dealing with rare or newly emerging diseases and specific combinations of comorbidities. We present MedAssist, a large language model (LLM)-empowered medical assistant designed to support the scrutinization and comprehension of electronic health records (EHRs). MedAssist leverages two key components: medical knowledge retrieval, which retrieves the latest and most comprehensive medical knowledge snippets from the web, and data retrieval, which extracts diagnosis and treatment plans for similar patients from existing EHR databases. By integrating these capabilities into user-friendly interfaces, MedAssist bridges critical gaps in medical knowledge accessibility and understanding, and advances patient care in realistic clinical scenarios.
Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases
Authors: Chuang Zhao, Hui Tang, Jiheng Zhang, Xiaomeng Li
Venue: WWW
Year: 2025

Accurate healthcare prediction is essential for improving patient outcomes. Existing work primarily leverages advanced frameworks like attention or graph networks to capture the intricate collaborative (CO) signals in electronic health records. However, prediction for rare diseases remains challenging due to limited co-occurrence and inadequately tailored approaches. To address this issue, this paper proposes UDC, a novel method that unveils discrete clues to bridge consistent textual knowledge and CO signals within a unified semantic space, thereby enriching the representation semantics of rare diseases. Specifically, we focus on addressing two key sub-problems: (1) acquiring distinguishable discrete encodings for precise disease representation and (2) achieving semantic alignment between textual knowledge and the CO signals at the code level. For the first sub-problem, we refine the standard vector quantized process to include condition awareness. Additionally, we develop an advanced contrastive approach in the decoding stage, leveraging synthetic and mixed-domain targets as hard negatives to enrich the perceptibility of the reconstructed representation for downstream tasks. For the second sub-problem, we introduce a novel codebook update strategy using co-teacher distillation. This approach facilitates bidirectional supervision between textual knowledge and CO signals, thereby aligning semantically equivalent information in a shared discrete latent space. Extensive experiments on three datasets demonstrate our superiority.
Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features
Authors: Muhammad Imran, Abdul Wahab Ziaullah, Kai Chen, Ferda Ofli
Venue: WWW
Year: 2025

The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities. However, the data from these platforms is often noisy, requiring automated methods to filter relevant information. Traditionally, supervised machine learning models have been used, but they lack generalizability. In contrast, Large Language Models (LLMs) show better capabilities in understanding and processing natural language out of the box. This paper provides a detailed analysis of the performance of six well-known LLMs in processing disaster-related social media data from a large-set of real-world events. Our findings indicate that while LLMs, particularly GPT-4o and GPT-4, offer better generalizability across different disasters and information types, most LLMs face challenges in processing flood-related data, show minimal improvement despite the provision of examples (i.e., shots), and struggle to identify critical information categories like urgent requests and needs. Additionally, we examine how various linguistic features affect model performance and highlight LLMs' vulnerabilities against certain features like typos. Lastly, we provide benchmarking results for all events across both zero- and few-shot settings and observe that proprietary models outperform open-source ones in all tasks.
SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks
Authors: Zhenhua Huang, Wenhao Zhou, Yufeng Li, Xiuyang Wu, Chengpei Xu, Junfeng Fang, Zhaohong Jia, Linyuan Lü, Feng Xia
Venue: WWW
Year: 2025

Heterogeneous Graph Neural Networks (HGNNs) are extensively applied in modeling web-based applications that involve heterogeneous graph structures. Explanation models for HGNNs aim to address their ''black box'' nature. Enhancing the interpretability of HGNNs leads to a better understanding and can potentially improve predictive performance. However, existing post-hoc HGNN explanation methods cannot impact the HGNN's predictions. Self-explainable homogeneous models also perform poorly on heterogeneous graphs. To address these challenges, we present a Self-Explainable Heterogeneous Graph Neural Network (SEHG), a novel architecture that integrates explanation generation into the learning process of HGNN through two alternative stages. The first stage focuses on producing high-quality explanations while providing predictions alongside. The second stage enhances prediction accuracy by a contrastive learning strategy. Unlike the current methods that rely on manually defined metapaths for structural explanations, SEHG generates important structure and feature explanations by learnable heterogeneous masks. To ensure high-quality and sparsity explanation, these masks are regulated by a uniquely designed range-based penalty during training. Moreover, we introduce HetBA, a collection of synthetic heterogeneous datasets designed to quantify and visualize explanations or heterogeneous graphs. Extensive experiments demonstrate the effectiveness of SEHG, which surpasses strong baselines in real-world node classification tasks by notable margins of up to 3.91%. SEHG also achieves state-of-the-art performance on synthetic datasets with improvement of up to 9.44%, and records the highest fidelity scores in explanation tasks, improving by up to 46.57%. To our knowledge, SEHG is a pioneering self-explainable HGNN framework that achieves state-of-the-art performance on both heterogeneous graph explanation and prediction tasks.
Explainable Multi-Modality Alignment for Transferable Recommendation
Authors: Shenghao Yang, Weizhi Ma, Zhiqiang Guo, Min Zhang, Haiyang Wu, Junjie Zhai, Chunhui Zhang, Yuekui Yang
Venue: WWW
Year: 2025

With the development of multi-modal modeling techniques, recent sequential recommender systems enhance transferability by incorporating cross-domain universal multi-modal data, e.g., text and image. Existing methods typically adopt pairwise alignment to alleviate the gap between modalities. However, this alignment paradigm has limitations on explainability, consistency, and expansibility, resulting in suboptimal performance. This paper proposes a novel Explainable multi-modality Alignment method for transferable Rec ommender systems, i.e., EARec. Specifically, we design a two-stage framework to achieve explainable modality alignment in the source domain and recommendation based on aligned modality representations in the target domain. In the first stage, we adopt a generative task to align various modalities in parallel to a shared anchor with explainable meaning. All modalities share the same anchor to ensure consistent direction. Additionally, we treat behavior as an independent modality to integrate task-specific information into the alignment framework. In the second stage, we compose multiple item modality representation models trained in the first stage to obtain a unified model capable of understanding various modalities simultaneously, thereby providing high-quality item modality representations for recommendations in the target domain. Benefiting from the approach of parallel modality alignment followed by model composition, the framework shows flexibility in expanding new modalities. Experimental results on multiple public datasets demonstrate the superiority of EARec over baselines, and further analyses indicate the explainability and expansibility of the proposed alignment method.
Economy Watchers Survey Provides Datasets and Tasks for Japanese Financial Domain
Authors: Masahiro Suzuki, Hiroki Sakaji
Venue: WWW
Year: 2025

Natural language processing (NLP) tasks in English and general domains are widely available and are often used to evaluate pre-trained language models. In contrast, fewer tasks are available for languages other than English and in the financial domain. Particularly, tasks in the Japanese and financial domains are limited. We develop two large datasets using data published by a Japanese central government agency. The datasets provide three Japanese financial NLP tasks, including 3- and 12-class classifications for categorizing sentences, along with a 5-class classification task for sentiment analysis. Our datasets are designed to be comprehensive and updated by leveraging an automatic update framework that ensures that the latest task datasets are publicly always available.
FAST-Q: Fast-track Exploration with Adversarially Balanced State Representations for Counterfactual Action Estimation in Offline Reinforcement Learning
Authors: Pulkit Agrawal, Rukma Talwadker, Aditya Pareek, Tridib Mukherjee
Venue: WWW
Year: 2025

Recent advancements in state-of-the-art (SOTA) offline reinforcement learning (RL) have primarily focused on addressing function approximation errors, which contribute to the overestimation of Q-values for out-of-distribution actions - a challenge that static datasets exacerbate. However, high-stakes applications such as recommendation systems in online gaming, introduce further complexities due to players' psychology/ intent driven by gameplay experiences and the platform's inherent volatility. These factors create highly sparse, partially overlapping state spaces across policies, further influenced by the experiment path selection logic which biases state spaces towards specific policies. Current SOTA methods constrain learning from such offline data by clipping known counterfactual actions as out-of-distribution due to poor generalization across unobserved states. Further aggravating conservative Q-learning and necessitating more online exploration. FAST-Q introduces a novel approach that (1) leverages Gradient Reversal Learning to construct balanced state representations, regularizing the policy-specific bias between the players' state and action thereby enabling counterfactual estimation; (2) supports offline counterfactual exploration in parallel with static data exploitation; and (3) proposes a Q-value decomposition strategy for multi-objective optimization, facilitating explainable recommendations over short and long-term objectives. These innovations demonstrate superiority of FAST-Q over prior SOTA approaches and demonstrates at least 0.15% increase in player returns, 2% improvement in lifetime value (LTV), 0.4% enhancement in the recommendation driven engagement, 2% improvement in the players' platform dwell time and an impressive 10% reduction in the costs associated with the recommendation, on our volatile gaming platform.
Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation
Authors: Weiming Liu, Chaochao Chen, Jiahe Xu, Xinting Liao, Fan Wang, Xiaolin Zheng, Zhihui Fu, Ruiguang Pei, Jun Wang
Venue: WWW
Year: 2025

Cross-Domain Recommendation (CDR) has been widely investi- gated for solving long-standing data sparsity problem via knowl- edge sharing across domains. In this paper, we focus on the Multi- Modal Cross-Domain Recommendation (MMCDR) problem where different items have multi-modal information while few users are overlapped across domains. MMCDR is particularly challenging in two aspects: fully exploiting diverse multi-modal information within each domain and leveraging useful knowledge transfer across domains. However, previous methods fail to cluster items with similar characteristics while filtering out inherit noises within different modalities, hurdling the model performance. What is worse, conventional CDR models primarily rely on overlapped users for domain adaptation, making them ill-equipped to handle scenarios where the majority of users are non-overlapped. To fill this gap, we propose Joint Similarity Item Exploration and Overlapped User Guidance (SIEOUG) for solving the MMCDR problem. SIEOUG first proposes similarity item exploration module, which not only obtains pair-wise and group-wise item-item graph knowledge, but also reduces irrelevant noise for multi-modal modeling. Then SIEOUG proposes user-item collaborative filtering module to aggregate user/item embeddings with the attention mechanism for collaborative filtering. Finally SIEOUG proposes overlapped user guidance module with optimal user matching for knowledge sharing across domains. Our empirical study on Amazon dataset with several different tasks demonstrates that SIEOUG significantly outperforms the state-of-the-art models under the MMCDR setting.
Time Series Analysis in the Web: Recent Advances and Future Trends
Authors: Ming Jin, Wei Jin, Hao Xue, Flora Salim, Yuxuan Liang
Venue: WWW
Year: 2025

Time series analysis has become instrumental in tackling key challenges in web-based applications, such as server load balancing, anomaly detection in e-commerce traffic, and tourism demand forecasting. This proposal introduces a comprehensive half-day lecture-style tutorial for The Web Conference 2025, tailored to professionals, researchers, and practitioners aiming to harness machine learning for analyzing web-sourced time series data. The tutorial will cover foundational principles, data processing techniques, and advanced modeling strategies, equipping attendees with both theoretical understanding and practical skills. Participants will also explore best practices for integrating time series analysis into web-centric workflows, with diverse applications in e-commerce, digital health, and transportation. Led by leading experts in the field, this tutorial provides an invaluable opportunity to deepen knowledge, gain hands-on experience, and foster meaningful connections-bridging the gap between theory and real-world implementation.
NoTeNet: Normalized Mutual Information-Driven Tuning-free Dynamic Dependence Network Inference Method for Multimodal Data
Authors: Xiao Tan, Yangyang Shen, Yan Zhang, Jingwen Shao, Dian Shen, Meng Wang, Beilun Wang
Venue: WWW
Year: 2025

Dynamic Dependence Network (DDN) inference is crucial for understanding evolving relationships in multimodal time series web data, with broad applications in fields like medical and financial network analysis. The inherent dynamic nature, temporal continuity, and heterogeneous data sources in multimodal time series data pose three fundamental challenges: computational efficiency, prediction stability and robustness, and modality quality disparity. Previous methods, generally lacking utilization of multiple modalities, either struggle with computational efficiency due to the time-intensive manual hyperparameter tuning, or compromise prediction stability and robustness by neglecting temporal coherence. To address these challenges, we propose a Normalized mutual information-driven Tuning-free Dynamic Dependence Network inference method for multimodal data, namely NoTeNet. NoTeNet provides a promising paradigm that can integrate two different data modalities to enhance prediction accuracy. It uses normalized mutual information transforms noisy auxiliary data into relationship matrices and employs a kernel function for smooth temporal estimation. Additionally, NoTeNet significantly reduces the need for manual hyperparameter adjustments, offering a tuning-free approach with theoretical guarantees. On various synthetic datasets and real-world data, NoTeNet demonstrates superior prediction accuracy and efficiency without the need for hyperparameter tuning, making it potential for a wide range of web data applications.
The Workshop of Artificial Intelligence for Web-Centric Time Series Analysis (AI4TS): Theory, Algorithms, and Applications
Authors: Ming Jin, Mahsa Salehi, Yuxuan Liang, Dongjin Song, Flora Salim, Min Wu, Shirui Pan, Qingsong Wen
Venue: WWW
Year: 2025

The Artificial Intelligence for Web-Centric Time Series Analysis (AI4TS) Workshop at The Web Conference 2025 aims to unite experts from academia and industry to advance artificial intelligence for web-centric time series analysis. This full-day workshop will feature at least five keynote talks, paper presentations, and a panel discussion, engaging attendees in topics that span theory, algorithms, and applications within web-based time series analysis. With a focus on web-sourced data from IoT devices, social media, and cloud platforms, the workshop addresses pressing challenges such as anomaly detection, forecasting, classification, and generation across diverse fields, including finance, healthcare, transportation, and biological sciences. AI researchers, practitioners, and policymakers are anticipated to join, fostering a collaborative environment to explore innovative approaches in time series for web applications. Building on the success of prior AI4TS workshops, this event will continue to honor outstanding research contributions. Supported by organization and committee members from both industry and academia, this will mark a new instance of the AI4TS series, following previous workshops hosted with KDD, ICDM, AAAI, IJCAI, etc., each drawing over 80 participants on average.
Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism
Authors: Jun Yin, Zhengxin Zeng, Mingzheng Li, Hao Yan, Chaozhuo Li, Weihao Han, Jianjin Zhang, Ruochen Liu, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Shirui Pan, Senzhang Wang
Venue: WWW
Year: 2025

Owing to the unprecedented capability in semantic understanding and logical reasoning, large language models (LLMs) have shown fantastic potential in developing next-generation sequential recommender systems (RSs). However, existing LLM-based sequential RSs mostly separate index generation from sequential recommendation, leading to insufficient integration between semantic information and collaborative information. On the other hand, the neglect of user-related information hinders LLM-based sequential RSs from exploiting high-order user-item interaction patterns. In this paper, we propose the End-to-End Dual Dynamic (ED2) recommender, the first LLM-based sequential RS which adopts dual dynamic index mechanism, targeting resolving the above limitations simultaneously. The dual dynamic index mechanism can not only assembly index generation and sequential recommendation into a unified LLM-backbone pipeline, but also make it practical for LLM-based sequential recommender to take advantage of user-related information. Specifically, to facilitate the LLM comprehension ability to dual dynamic index, we propose a multigrained token regulator which constructs alignment supervision based on LLMs semantic knowledge across multiple representation granularities. Moreover, the associated user collection data and a series of novel instruction tuning tasks are specially customized to capture the high-order user-item interaction patterns. Extensive experiments on three public datasets demonstrate the superiority of ED2, achieving an average improvement of 19.62% in Hit-Rate and 21.11% in NDCG.
SmartMem: Memory Failure Prediction Challenge at WWW 2025
Authors: Min Zhou, Hongyi Xie, Qiao Yu, Jialiang Yu, Zhenli Sheng
Venue: WWW
Year: 2025

Uncorrectable Errors (UEs) of Dynamic Random Access Memory (DRAM) have been identified as a major failure cause in data centers and the failure of High Bandwidth Memory (HBM), which severely threatens the availability and reliability of cloud service, entire computing clusters, and web-based applications. Forecasting UEs before enacting preemptive maintenance measures has emerged as a viable strategy for diminishing server outages and some machine-learning-based solutions have also been proposed. However, predicting UEs presents several challenges: data noise and extreme imbalance as the UEs are exceedingly rare in memory events; heterogeneous data sources as the DRAMs in the field come from different manufacturing or architecture platforms; distribution shifts due to the hardware aging; and latent factors due to the dynamic access mechanism. These challenges become even more pronounced in web-centric contexts, where UE patterns can vary significantly across applications, including content delivery networks, e-commerce platforms, and real-time analytics.We developed a comprehensive real-world memory error dataset that includes both micro-level and bit-level information. This dataset presents a two-stage challenge aimed at devising a more efficient and generalized solution for event prediction. We believe the competition and dataset provide a breeding ground to foster discussions and further progress on several important research topics towards real-world ML applications. More details can be found in the competition homepage: https://hwcloud-ras.github.io/SmartMem.github.io/
Generative Recommendation: Towards Personalized Multimodal Content Generation
Authors: Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Tat-Seng Chua
Venue: WWW
Year: 2025

Traditional recommender systems primarily retrieve items from a pre-defined item corpus for personalized recommendations. However, this a retrieval-based paradigm faces two major constraints: 1) the existing item corpus with human-generated items may not adequately satisfy users' diverse information needs, and 2) users often rely on passive and noisy feedback (e.g., clicks) to refine the recommendations. To overcome the limitations, the rapid advancement of AI-Generated Content (AIGC) presents significant potential: 1) generative AI promotes the creation of personalized multimodal content as new items to satisfy users' information needs, and 2) large language models reduce the efforts of users to express information needs proactively in natural language. In this light, we propose a novel Generative Recommender paradigm named GeneRec with two objectives: 1) generating personalized multimodal content, and 2) integrating proactive user instructions to guide content generation. To achieve the objectives, GeneRec introduces an AI generator to personalize multimodal content generation and leverages user instructions to obtain users' information needs. Specifically, GeneRec first employs an instructor to pre-process users' instructions and traditional feedback (e.g., clicks) to extract generation guidance. Following the guidance, we develop the AI generator with an AI editor to edit existing items and an AI creator to create new items, respectively. Lastly, we study the feasibility of implementing the AI creator in the fashion domain, showing promising results. Furthermore, to ensure the trustworthiness of the generated items, we emphasize various trustworthiness checks such as fairness and safety checks.