OpenRaiser/Intern-Atlas huggingface dataset:
Beast in the Cage: A Fine-grained and Object-oriented Permission System to Confine JavaScript Operations on the Web
Authors: Rui Zhao
Venue: WWW
Year: 2025

JavaScript plays a crucial role on web. However, the inclusion of unknown, vulnerable, and malicious scripts on websites and in browser extensions and the use of browsers' developer tools often lead to undesired web content manipulations and data acquisitions. To restrict JavaScript operations on web content and data, we introduce a fine-grained, mandatory access control-based, and object-oriented permission system to browsers. With our system, web developers can define policies for sensitive web elements on their web pages to allow or deny scripts' operations on web content and data within browsers. The system substantially thwarts many web threats and attacks, and offers benefits to personal data governance. We developed a tool for automatic policy generation and demonstrated the usability and compatibility of the system in a three-month study. Our system is a reasonable and practical solution, bolstering the security and trustworthiness on the internet.
AC-TKG+: Actor-Critic with Multi-Level Loss for Effective Few-shot Inductive Learning on Temporal Knowledge Graphs
Authors: Mingyuan Zhao, Jiafeng Gu, Wenjie Zhang
Venue: WWW
Year: 2025

This paper presents an enhanced approach for few-shot temporal knowledge graph reasoning based on an Actor-Critic framework. Through the integration of discrete entity-level supervision, continuous state-value estimation, and path-based global signals from Dijkstra's algorithm, the method effectively addresses the optimization challenges in existing approaches. Our tri-level loss architecture enables comprehensive learning at entity selection, state evaluation, and global path assessment levels, while maintaining dynamic exploration-exploitation balance. Empirical evaluations demonstrate the superiority of the proposed method over current state-of-the-art baselines across multiple performance metrics, with notably substantial gains observed in sparse-data scenarios.
MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot
Authors: Xuejiao Zhao, Siyan Liu, Su-Yin Yang, Chunyan Miao
Venue: WWW
Year: 2025

Retrieval-augmented generation (RAG) is a well-suited technique for retrieving privacy-sensitive Electronic Health Records (EHR). It can serve as a key module of the healthcare copilot, helping reduce misdiagnosis for healthcare practitioners and patients. However, the diagnostic accuracy and specificity of existing heuristic-based RAG models used in the medical domain are inadequate, particularly for diseases with similar manifestations. This paper proposes MedRAG, a RAG model enhanced by knowledge graph (KG)-elicited reasoning for the medical domain that retrieves diagnosis and treatment recommendations based on manifestations. MedRAG systematically constructs a comprehensive four-tier hierarchical diagnostic KG encompassing critical diagnostic differences of various diseases. These differences are dynamically integrated with similar EHRs retrieved from an EHR database, and reasoned within a large language model. This process enables more accurate and specific decision support, while also proactively providing follow-up questions to enhance personalized medical decision-making. MedRAG is evaluated on both a public dataset DDXPlus and a private chronic pain diagnostic dataset (CPDD) collected from Tan Tock Seng Hospital, and its performance is compared against various existing RAG methods. Experimental results show that, leveraging the information integration and relational abilities of the KG, our MedRAG provides more specific diagnostic insights and outperforms state-of-the-art models in reducing misdiagnosis rates. Our code will be available at https://github.com/SNOWTEAM2023/MedRAG
Hierarchical Multi-Label Classification of Building Management System Time-Series Data
Authors: Haokai Zhao, Jonas Macken, Leo Dinendra, Yaqing He, Ruiyuan Yang
Venue: WWW
Year: 2025

The Brick by Brick (BBB) challenge focuses on automating the classification of building management system (BMS) time-series data to enhance energy efficiency and operational insights. This dataset presents a multi-label hierarchical classification problem, requiring models to capture dependencies among labels. Our solution involves extracting statistical and spectral features from raw time-series data and expanding the training set using a splitting-based augmentation technique, increasing its size 28-fold. Labels are organised into five hierarchical tiers, and we train an ensemble of Random Forest (RF) and XGBoost (XGB) models using 10-fold cross-validation and sequential tiered predictions. Our submission achieves a macro F1-score of 0.528 on the public + private data leaderboard, highlighting the effectiveness of hierarchical ensemble learning and data augmentation for multi-label classification in smart building applications.
A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems
Authors: Binglei Zhao, Houying Qi, Guang Xu, Mian Ma, Xiwei Zhao, Feng Mei, Sulong Xu, Jinghe Hu
Venue: WWW
Year: 2025

Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With strict latency requirements, pre-ranking utilizes lightweight models to perform a preliminary selection from massive retrieved candidates. However, recent works focus solely on improving consistency with ranking, relying exclusively on downstream stages. Since downstream input is derived from the pre-ranking output, they will exacerbate the sample selection bias (SSB) issue and Matthew effect, leading to sub-optimal results. To address the limitation, we propose a novel Hybrid Cross-Stage Coordination Pre-ranking model (HCCP) to integrate information from upstream (retrieval) and downstream (ranking, re-ranking) stages. Specifically, cross-stage coordination refers to the pre-ranking's adaptability to the entire stream and the role of serving as a more effective bridge between upstream and downstream. HCCP consists of Hybrid Sample Construction and Hybrid Objective Optimization. Hybrid sample construction captures multi-level unexposed data from the entire stream and rearranges them to become the optimal guiding ''ground truth'' for pre-ranking learning. Hybrid objective optimization contains the joint optimization of consistency and long-tail precision through our proposed Margin InfoNCE loss. It is specifically designed to learn from such hybrid unexposed samples, improving the overall performance and mitigating the SSB issue. The appendix describes a proof of the efficacy of the proposed loss in selecting potential positives. Extensive offline and online experiments indicate that HCCP outperforms SOTA methods by improving cross-stage coordination. It contributes up to 14.9% UCVR and 1.3% UCTR in the JD E-commerce recommendation system. Concerning code privacy, we provide a pseudocode for reference.
AI in Healthcare for Resource Limited Settings: An Exploration and Ethical Evaluation
Authors: Rune Chi Zhao, Xiuyuan Yuan
Venue: WWW
Year: 2025

Advances in artificial intelligence (AI) hold great promise to transform healthcare in resource-limited settings (RLS). However, due to challenges such as shortages in healthcare professionals, data scarcity, and inadequate regulatory frameworks, RLS are left especially vulnerable to AI's potential risks and ethical violations. Thus, despite rising expectations, substantial gaps remain in our understanding of how to responsibly integrate AI into the healthcare systems of RLS. In response, this review critically examines AI healthcare applications in RLS, with the intention of promoting ethical, transparent, and secure principles for future implementations. We first provide an exploration of the potential uses of AI in resource-limited healthcare and present four broad subfields: decision support, predictive analytics, telemedicine and digital health tools, and resource management. Taking an analytical approach, we illustrate both the potential benefits and hidden ethical pitfalls that may arise when implementing AI in contexts with limited human and financial resources. Drawing on both recent studies and original perspectives, we aim to provide an overview of major ethical concerns in an RLS context - including algorithmic bias, non-maleficence, privacy and security, autonomy, and transparency - as well as discussing additional ethical dilemmas rarely addressed in literature. Subsequently, we advocate for context-specific regulations and culturally sensitive frameworks, in addition to robust oversight and active local participation. Finally, we provide recommendations that aim to protect patient welfare, uphold autonomy, and promote equity-so that AI applications ultimately strengthen, rather than undermine, global efforts to reduce healthcare disparities.
USHB: A Unified Framework for Simulating Human Behaviors in Agent Society through User-and-Item Modeling: Unified Framework for Simulating Human Behaviors
Authors: Renhuo Zhao, Hailong Yang, Mingxian Gu, Jianqi Wang, Wu Long, Zhaohong Deng
Venue: WWW
Year: 2025

Emulating real-world human behaviors within Artificial Intelligent (AI) agent systems continues to be a formidable challenge, as existing methodologies frequently have difficulty integrating the intricacies of real-world scenarios and personal preferences.To address this issue, we propose USHB, a mulit-agent framework that emphasizes advanced user and item modeling along with communication style simulation. USHB consists of 3 modules: a Knowledge-Mining Module (KMM), a User-and-Item Modeling Module (UIMM), and a Reasoning Module (RM). USHB utilizes Large Language Models (LLMs) to predict responses, reviews, and ratings tailored to individuals, guaranteeing results that are both coherent and contextually appropriate. USHB is capable of delivering precise, detailed simulations that closely mimic human behavior. We evaluated USHB using datasets from Yelp, Amazon, and Goodreads, where it consistently outperformed baseline methods. Moreover, USHB demonstrated strong generalization and maintained stable performance across a variety of model configurations. These advancements make USHB a valuable contribution to dynamic, context-aware behavior simulation, achieving a top-three ranking in the 2025 AgentSociety Challenge. Our codes are available at https://github.com/jnuaipr/AgentsChallenge.
DVIB: Towards Robust Multimodal Recommender Systems via Variational Information Bottleneck Distillation
Authors: Wenkuan Zhao, Shanshan Zhong, Yifan Liu, Wushao Wen, Jinghui Qin, Mingfu Liang, Zhongzhan Huang
Venue: WWW
Year: 2025

In multimodal recommender systems (MRS), integrating various modalities helps to model user preferences and item characteristics more accurately, thereby assisting users in discovering items that match their interests. Although the introduction of multimodal information offers opportunities for performance improvement, it will increase the risks of inherent noise and information redundancy, posing challenges to the robustness of MRS. Many existing methods typically address these two issues separately either by introducing perturbations at the model input for robust training to handle noise or by designing complex network structures to filter out redundant information. In contrast, we propose the DVIB framework to simultaneously address both issues in a simple manner. We found that moving the perturbations from the input layer to the hidden layer, combined with feature self-distillation, can mitigate noise and handle information redundancy without altering the original network architecture. Additionally, we also provide theoretical evidence for the effectiveness of DVIB, demonstrating that the framework not only explicitly enhances the robustness of model training but also implicitly exhibits an information bottleneck effect, which effectively reduces redundant information during multimodal fusion and improves feature extraction quality. Extensive experiments show that DVIB consistently improves the performance of MRS across different datasets and model settings, and it can complement existing robust training methods, representing a promising new paradigm in MRS. See code at https://github.com/MarshmallowLight/DVIB.git.
Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning
Authors: Gang Zhao, Ximing Zhang, Chenji Lu, Hui Zhao, Tianshu Wu, Pengjie Wang, Jian Xu, Bo Zheng
Venue: WWW
Year: 2025

Effective query-item relevance modeling is pivotal for enhancing user experience and safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the vast inherent knowledge, Large Language Model (LLM) approach demonstrates strong performance and long-tail generalization ability compared with previous neural-based specialized relevance learning methods. Though promising, current LLM-based method encounters the following inadequacies in practice: Firstly, the relevance modeling process is a black box, making it difficult to clearly understand why LLM can provide the significant improvement or to analyze its relevance judgment errors. This opacity also hinders the reuse of the LLM's rich intrinsic knowledge. Secondly, the massive parameters and computational demands make it challenging to be deployed online. To improve the interpretability of LLM and boost the performance of online relevance models, we propose an Explainable LLM-driven Multi-dimensional Distillation framework for e-commerce relevance learning, which comprises two core components: (1) An Explainable LLM for relevance modeling (ELLM-rele), which decomposes the relevance learning into intermediate steps and models relevance learning as a Chain-of-Thought (CoT) reasoning, thereby enhancing both interpretability and performance of LLM. (2) A Multi-dimensional Knowledge Distillation (MKD) architecture that transfers the knowledge of ELLM-rele to current interaction-based and representation-based student models from both the relevance score distribution and CoT reasoning aspects. Through distilling the probabilistic and CoT reasoning knowledge, MKD improves both the semantic interaction and long-tail generalization abilities of student models. Extensive offline evaluations and online experiments conducted on Taobao search ad scene demonstrate that our proposed ELLM-MD framework significantly enhances e-commerce relevance learning performance and consumer experience.
Hierarchical Sequence ID Representation of Large Language Models for Large-scale Recommendation Systems
Authors: Rui Zhao, Rui Zhong, Haoran Zheng, Wei Yang, Chi Lu, Beihong Jin, Peng Jiang, Kun Gai
Venue: WWW
Year: 2025

Recently, large language models (LLMs) have shown great potential in recommendation systems (RS), however, there are still challenges in applying LLMs to large-scale RS. One major challenge is to effectively transform unique identities (IDs) in RS into a suitable training space for LLMs. Current approaches either lack collaborative signals of user behaviors, or they represent IDs with a single token, which fail to align with the autoregressive paradigm of LLMs. Consequently, the potential of IDs is not fully utilized in LLMs for recommendation, particularly in large-scale systems with numerous users, items, and sparse interactions. On the other hand, when enhancing large-scale RS, the high-dimensional representations from the last hidden layer of LLMs pose difficulties for meeting the low-latency requirements. In this paper, we propose a Hierarchical sequence ID representation method for applying LLMs to Recommender systems, called HI-RecM. Specifically, we design a tree-based ID encoding method that generates hierarchical sequence indices, then we embed the indices token-by-token into LLMs to align LLMs' autoregressive training paradigm. We design a joint dimension reduction (JDR) module to efficiently extract information, enabling lossless dimension reduction of high-dimensional LLM representations. Extensive experiments show that our method can outperform state-of-the-art methods. Moreover, HI-Rec has been deployed on Kuaishou's online advertising platform, achieving 2.1% increase of revenue. We also release the implementation code in https://github.com/Rita-73/HI-Rec.
Zero-Shot Multi-View Australian Sign Language Recognition
Authors: Zhongtian Zheng
Venue: WWW
Year: 2025

Sign language is a common method of communication used by deaf individuals or those with speech impairments, possessing a complexity comparable to spoken language. As a complete and sophisticated language, mastering sign language is no simple task. Sign Language Recognition (SLR) aims to reduce communication barriers between the deaf and others by interpreting gestures in sign language videos. However, due to the scarcity of multi-view annotated datasets, most current models are only capable of recognising hand gestures from frontal views. Performance can significantly drops when processing non-frontal views due to issues like hand occlusion. To address this issue, we introduce view-conditioned diffusion models for the SLR task. Leveraging the strong zero-shot generalisation capability of large-scale diffusion models, we synthesise multiple side-view pose estimations to enhance the diversity of training data. Additionally, we integrate the RGB modality to enrich skeleton-based methods with a broader contextual perspective. Experiments on the MM-WLAuslan dataset demonstrate that our method significantly improves the robustness of SLR models when processing multi-view input. In the Cross-View Isolated Sign Language Recognition (CV-ISLR) Challenge, we achieved second place in the RGB setting.
Cluster Aware Graph Anomaly Detection
Authors: Lecheng Zheng, John R. Birge, Haiyue Wu, Yifang Zhang, Jingrui He
Venue: WWW
Year: 2025

Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity. Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity. However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditional approaches. Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs. To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE. Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label (i.e., soft membership assignments) without any strong assumption about the graph. To mitigate potential biases from the pseudo-label, we introduce a similarity-guided loss. Theoretically, we show that the proposed similarity-guided loss is a variant of contrastive learning loss, and we present how this loss alleviates the bias introduced by pseudo-label with the connection to graph spectral clustering. Experimental results on several datasets demonstrate the effectiveness and efficiency of our proposed framework. Specifically, CARE outperforms the second-best competitors by more than 39% on the Amazon dataset with respect to AUPRC and 18.7% on the YelpChi dataset with respect to AUROC. The code of our method is available at the GitHub link: https://github.com/zhenglecheng/CARE-demo.
A Responsible and Extendable Context-Aware Recommender System
Authors: Xiangmin Zhou, Lei Chen, Chengkun He, Junfeng Wu, Weiyi Zhou, Jie Shao, Yanchun Zhang
Venue: WWW
Year: 2025

Context-aware social media recommendation has been important in many applications such as e-commerce and entertainment. However, existing systems consider pre-specified contexts and cannot well handle user preferences, which negatively affects the recommendation quality and efficiency, and causes them not extendable to various applications. In this demo, we design RECARS, the first responsible and extendable context-aware recommender system. RECARS is designed with novel techniques, including efficient data organization over MongoDB and Apache Flink, and effective responsible recommendation generation that supports the system interactions with users. It allows users to perform iteratively refining and explaining the results by active learning and large language model (LLM). We demonstrate the usage of RECARS via YouTube.
When Large Vision Language Models Meet Multimodal Sequential Recommendation: An Empirical Study
Authors: Peilin Zhou, Chao Liu, Jing Ren, Xinfeng Zhou, Yueqi Xie, Meng Cao, Zhongtao Rao, You-Liang Huang, Dading Chong, Junling Liu, Jae Boum Kim, Shoujin Wang, Raymond Chi-Wing Wong, Sunghun Kim
Venue: WWW
Year: 2025

As multimedia content continues to grow on the web, the integration of visual and textual data has become a crucial challenge for web applications, particularly in recommendation systems. Large Vision Language Models (LVLMs) have demonstrated considerable potential in addressing this challenge across various tasks that require such multimodal integration. However, their application in multimodal sequential recommendation (MSR) has not been extensively studied. To bridge this gap, we introduce MSRBench, the first comprehensive benchmark designed to systematically evaluate different LVLM integration strategies in web-based recommendation scenarios. We benchmark three state-of-the-art LVLMs, i.e., GPT-4 Vision, GPT-4o, and Claude-3-Opus, on the next item prediction task using the constructed Amazon Review Plus dataset, which includes additional item descriptions generated by LVLMs. Our evaluation examines five integration strategies: using LVLMs as recommender, item enhancer, reranker, and various combinations of these roles. The benchmark results reveal that 1) using LVLMs as rerankers is the most effective strategy, significantly outperforming others that rely on LVLMs to directly generate recommendations or only enhance items; 2) GPT-4o consistently achieves the best performance across most scenarios, particularly when employed as a reranker; 3) the computational inefficiency of LVLMs presents a major barrier to their widespread adoption in real-time multimodal recommendation systems. Our code and datasets are available at https://github.com/PALIN2018/MSRBench.
Training-free Graph Anomaly Detection: A Simple Approach via Singular Value Decomposition
Authors: Cheng Zhou, Guangxia Li, Hao Weng, Yiyu Xiang
Venue: WWW
Year: 2025

Graph anomaly detection (GAD) is essential for identifying irregular behavior within graphs. Recent advances in GAD rely on deep learning techniques and have shown promise. However, prior deep learning-based GAD methods suffer from various limitations such as low accuracy, long training time, and limited scalability. To tackle these limitations, we propose TFGAD, a training-free graph anomaly detection approach. Our main idea is to process node attributes and local structures separately using distinct matrices, which are optimally determined via singular value decomposition, thus eliminating the need for additional training. For anomaly detection, we propose a lightweight scoring function that combines the reconstruction errors of node attributes with the projection lengths of local structures to quantify node abnormalities. Extensive experiments demonstrate that TFGAD significantly outperforms state-of-the-art deep learning-based baselines while reducing runtime and memory overhead. The results highlight TFGAD's potential as an effective and efficient solution for GAD, particularly in scenarios where computational resources are constrained.