OpenRaiser/Intern-Atlas huggingface dataset:
Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health Crisis
Authors: Limiao Zhang, Xinyang Qi, Haiping Ma, Jie Gao, Xingyi Zhang, Yanqing Hu, Yaochu Jin
Venue: WWW
Year: 2025

The 21st century has already witnessed so many outbreaks with pandemic potential, including SARS (2002), H1N1 (2009), MERS (2012), Ebola (2014), Zika virus (2015), and the COVID-19 pandemic (2019). Using 60 million geotagged Sina Weibo tweets covering over 20 million active accounts, we investigate the collective emotional dynamics on social media in the most recent global pandemic, i.e., COVID-19. This research features two highlights: (1) It focuses on the Chinese population located in the initial epicenter of the pandemic. (2) It examines the initial year after the pandemic outbreak, a critical period where emotions were most intense due to the uncertainty and rapid developments related to the crisis. Using cross-disciplinary methods, we reveal a positive connection between online emotional resonance and geographic proximity, demonstrating a direct mapping between virtual network distances and physical spatial embedding. We propose a percolation-based index to measure the nationwide emotional resonance level with which we illustrate the significant economic impact of the global health issue. Finally, we identify a leader-follower pattern in emotional resonance fluctuations based on time-lag emotion correlations, revealing that less active regions play a crucial role in leading and responding to emotional changes. In the face of long COVID and emerging global health crises, our analysis elucidates how collective emotional resonance evolves, providing potential directions for online opinion interventions during global shocks.
A Plug-in Critiquing Approach for Knowledge Graph Recommendation Systems via Representative Sampling
Authors: Huanyu Zhang, Xiaoxuan Shen, Baolin Yi, Jianfang Liu, Yinao Xie
Venue: WWW
Year: 2025

Incorporating a critiquing component into recommender applications facilitates the enhancement of user perception. Typically, critique-able recommender systems adapt the model parameters and update the recommendation list in real-time through the analysis of user critiquing keyphrases in the inference phase. The current critiquing methods necessitate the designation of a dedicated recommendation model to estimate user relevance to the critiquing keyphrase during the training phase preceding the recommendations update. This paradigm restricts the applicable scenarios and reduces the potential for keyphrase exploitation. Furthermore, these approaches ignore the issue of catastrophic forgetting caused by continuous modification of model parameters in multi-step critiquing. Thus, we present a general Representative Items Sampling Framework for Critiquing on Knowledge Graph Recommendation (RISC) implemented as a plug-in, which offers a new paradigm for critiquing in mainstream recommendation scenarios. RISC leverages the knowledge graph to sample important representative items as a hinge to expand and convey information from user critiquing, indirectly estimating the relevance of the user to the critiquing keyphrase. Consequently, the necessity for specialized user-keyphrase correlation modules is eliminated with respect to a variety of knowledge graph recommendation models. Moreover, we propose a Weight Experience Replay (WER) approach based on KG to mitigate catastrophic forgetting by reinforcing the user's prior preferences during the inference phase. Our extensive experimental findings on three real-world datasets and three knowledge graph recommendation methods illustrate that RISC with WER can be effectively integrated into knowledge graph recommendation models to efficiently utilize user critiquing for refining recommendations and mitigate catastrophic forgetting.
Spache: Accelerating Ubiquitous Web Browsing via Schedule-Driven Space Caching
Authors: Qi Zhang, Qian Wu, Zeqi Lai, Jihao Li, Hewu Li, Yuyu Liu, Yuanjie Li, Jun Liu
Venue: WWW
Year: 2025

In this paper, we perform a systematic study to explore a pivotal problem facing the web community: is current distributed web cache ready for future satellite Internet? First, through a worldwide performance measurement based on the RIPE Atlas platform and Starlink, the largest low-earth orbit (LEO) satellite network (LSN) today, we identify that the uneven deployment of current distributed cache servers, inter-ISP meandering routes and the last-mile congestion on LEO links jointly prevent existing terrestrial web cache from providing low-latency web access for users in emerging LSNs. Second, we propose Spache, a novel web caching system which addresses the limitations of existing ground-only cache by exploiting a bold idea: integrating web cache into LEO satellites to achieve ubiquitous and low-latency web services. Specifically, Spache leverages a key feature of LSNs called communication schedule to efficiently prefetch web contents on satellites, and adopts a schedule-driven partitioning strategy to avoid cache pollution involved by LEO mobility. Finally, we implement a prototype of Spache, and evaluate it based on real-world HTTP traces and data-driven LSN simulation. Extensive evaluations demonstrate that as compared to existing distributed caching solutions, Spache can improve cache hit ratio by 19.8% on average, reduce latency by up to 17.7%, and maintains consistently low web browsing latency for global LSN users.
The 1st Workshop on Human-Centered Recommender Systems
Authors: Kaike Zhang, Yunfan Wu, Yougang Lyu, Du Su, Yingqiang Ge, Shuchang Liu, Qi Cao, Zhaochun Ren, Fei Sun
Venue: WWW
Year: 2025

Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous challenges, such as the information cocoon effect, privacy concerns, fairness issues, and more. Consequently, this workshop aims to provide a platform for researchers to explore the development of Human-Centered Recommender Systems (HCRS). HCRS refers to the creation of recommender systems that prioritize human needs, values, and capabilities at the core of their design and operation. In this workshop, topics will include, but are not limited to, robustness, privacy, transparency, fairness, diversity, accountability, ethical considerations, and user-friendly design. We hope to engage in discussions on how to implement and enhance these properties in recommender systems. Additionally, participants will explore diverse evaluation methods, including innovative metrics that capture user satisfaction and trust. This workshop seeks to foster a collaborative environment for researchers to share insights and advance the field toward more ethical, user-centric, and socially responsible recommender systems.
Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition
Authors: Xu Zhang, Peng Wang, Chen Wang, Zhe Xu, Xiaohua Nie, Wei Wang
Venue: WWW
Year: 2025

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.
Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction
Authors: Guoxiao Zhang, Yi Wei, Yadong Zhang, Huajian Feng, Qiang Liu
Venue: WWW
Year: 2025

Click-Through Rate (CTR) prediction is essential in online advertising, where semantic information plays a pivotal role in shaping user decisions and enhancing CTR effectiveness. Capturing and modeling deep semantic information, such as a user's preference for ''Häagen-Dazs' HEAVEN strawberry light ice cream'' due to its health-conscious and premium attributes, is challenging. Traditional semantic modeling often overlooks these intricate details at the user and item levels. To bridge this gap, we introduce a novel approach that models deep semantic information end-to-end, leveraging the comprehensive world knowledge capabilities of Large Language Models (LLMs). Our proposed LLM-infused CTR prediction framework(Multi-level Deep Semantic Information Infused CTR model via Distillation, MSD) is designed to uncover deep semantic insights by utilizing LLMs to extract and distill critical information into a smaller, more efficient model, enabling seamless end-to-end training and inference. Importantly, our framework is carefully designed to balance efficiency and effectiveness, ensuring that the model not only achieves high performance but also operates with optimal resource utilization. Online A/B tests conducted on the Meituan sponsored-search system demonstrate that our method significantly outperforms baseline models in terms of Cost Per Mile (CPM) and CTR, validating its effectiveness, scalability, and balanced approach in real-world applications.
TSGGuide: Recommendation Guide for Multivariate Time Series Generation
Authors: Beining Zhang, Kai Wu, Xiaoyu Zhang, Handing Wang, Jing Liu, Bei Wang, Kai Wang
Venue: WWW
Year: 2025

Multivariate Time Series Generation (MTSG) plays a crucial role in time series analysis, supporting tasks such as data augmentation and anomaly detection. While several methods exist for MTSG, recommending the most suitable method for new scenarios remains a significant challenge. Although prior work by [2] provides guidance for selecting MTSG methods, it lacks coverage of recent diffusion-based methods and has limited exploration of channel-independent frameworks. We address these gaps by improving the recommendation guide, highlighting the effectiveness of a central discriminator within the channel-independent framework. Our revised guide makes three key recommendations: 1) VAE-based methods excel on small-scale datasets; 2) a channel-independent framework with the newly designed central discriminator is optimal in most cases; and 3) a diffusion-based method is preferable when ample data and computational resources are available.
REACT: Residual-Adaptive Contextual Tuning for Fast Model Adaptation in Threat Detection
Authors: Jiayun Zhang, Junshen Xu, Bugra Can, Yi Fan
Venue: WWW
Year: 2025

Web and mobile systems show constant distribution shifts due to the evolvement of services, users, and threats, severely degrading the performance of threat detection models trained on prior distributions. Fast model adaptation with minimal new data is essential for maintaining reliable security measures. A key challenge in this context is the lack of ground truth, which undermines the ability of existing solutions to align classes across shifted distributions. Moreover, the limited new data often fails to represent the underlying distribution, providing sparse and potentially noisy information for adaptation. In this paper, we propose REACT, a novel framework that adapts the model using a few unlabeled data and contextual insights. We leverage the inherent data imbalance in threat detection and meta-train weights on diverse unlabeled subsets to generalize common patterns across distributions, eliminating the reliance on labels for alignment. REACT decomposes a neural network into two complementary components: meta weights as a shared foundation of general knowledge, and residual adaptive weights as adjustments for specific shifts. To compensate for the limited availability of new data, REACT trains a hypernetwork to predict adaptive weights based on data and contextual information, enabling knowledge sharing across distributions. The meta weights and the hypernetwork are updated alternately, maximizing both generalization and adaptability. Extensive experiments across multiple datasets and models demonstrate that REACT improves AUROC by 14.85% over models without adaptation, outperforming the state-of-the-art.
Efficient Integration of ASR with Large Language Models to Enhance Video Search at Scale
Authors: Qiang Zhang, Fengshun Xiao, Tianjiao Li, Li Lin, Hanyin Fang, Huyang Sun, Ruoyu Liu, Xiaoyan Zhu, Jiayin Wang
Venue: WWW
Year: 2025

Currently, video is the most consumed content on the internet, with an increasing amount of information and knowledge shared as videos. This has led to a significant rise in video search requests. On Bilibili, one of China's largest knowledge-based video platforms, over half of daily users search for videos. Recent advancements in multimodal embeddings and text-to-video retrieval have shown promise, yet large-scale systems require search results within seconds. The high computational costs of image-based models have limited their scalability in video retrieval, with text-based searches remaining dominant and many video contents under-indexed. This paper presents a design that integrates ASR (Automatic Speech Recognition) text and uses large language models (LLMs) to optimize video search. ASR provides cost-effective video understanding and has been widely used for generating subtitles. However, in search scenarios, it suffers from noise and entity errors, impacting accuracy and leading to false retrieval. We leverage LLMs to generate high-quality text and summaries from original ASR text, integrating them into the video search engine to retrieval. LLMs are also used to enhance query understanding and relevance. Additionally, we enables direct answer generation from video summaries when watching is inconvenient for users. Offline evaluations and user experiments show significant improvements in search satisfaction while maintaining manageable computational costs. Deployed on Bilibili for a year, the enhanced video search engine has received daily feedback from millions of users, providing a best practice for using LLMs in video search and lessons for further optimization.
Pricing Items Based on Their Own History: Transformer-Generated Deep Isotonic Pricing Networks
Authors: Longxiang Zhang, Linlin Yu, Bin Chen, Zhuojun Wang, Chengfu Huo
Venue: WWW
Year: 2025

This paper introduces a dynamic pricing framework for e-commerce that leverages data science to optimize revenue. The framework employs a dual-module system combining demand forecasting and optimal pricing. Key innovations include 1)using a Transformer encoder to generate model weights based on historical data and 2)introducing a Double Weighted Mean Absolute Percentage Error to improve accuracy. Field experiments on 1688.com demonstrated increased platform profitability.
Edu-Values: Towards Evaluating the Chinese Education Values of Large Language Models
Authors: Peiyi Zhang, Yazhou Zhang, Bo Wang, Lu Rong, Prayag Tiwari, Jing Qin
Venue: WWW
Year: 2025

In this paper, we present Edu-Values, the first Chinese education values evaluation benchmark that includes seven core values: professional philosophy, teachers' professional ethics, education laws and regulations, cultural literacy, educational knowledge and skills, basic competencies and subject knowledge. We meticulously design 1,418 questions, covering multiple-choice, multi-modal question answering, subjective analysis, adversarial prompts, and Chinese traditional culture (short answer) questions. We conduct human feedback based automatic evaluation over 21 state-of-the-art (SoTA) LLMs, and highlight three main findings: (1) due to differences in educational culture, Chinese LLMs outperform English LLMs, with Qwen 2 ranking the first with a score of 81.37; (2) LLMs often struggle with teachers' professional ethics and professional philosophy; (3) leveraging Edu-Values to build an external knowledge repository for RAG significantly improves LLMs' alignment. This demonstrates the effectiveness of the proposed benchmark.
MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems
Authors: Yi Zhang, Yiwen Zhang
Venue: WWW
Year: 2025

The core of the general recommender systems lies in learning high-quality embedding representations of users and items to investigate their positional relations in the feature space. Unfortunately, data sparsity caused by difficult-to-access interaction data severely limits the effectiveness of recommender systems. Faced with such a dilemma, various types of self-supervised learning methods have been introduced into recommender systems in an attempt to alleviate the data sparsity through distribution modeling or data augmentation. However, most data augmentation relies on elaborate manual design, which is not only not universal, but the bloated and redundant augmentation process may significantly slow down model training progress. To tackle these limitations, we propose a novel Dual Mixing-based Recommendation Framework (MixRec) to empower data augmentation as we wish. Specifically, we propose individual mixing and collective mixing, respectively. The former aims to provide a new positive sample that is unique to the target (user or item) and to make the pair-wise recommendation loss benefit from it, while the latter aims to portray a new sample that contains group properties in a batch. The two mentioned mixing mechanisms allow for data augmentation with only one parameter that does not need to be set multiple times and can be done in linear time complexity. Besides, we propose the dual-mixing contrastive learning to maximize the utilization of these new-constructed samples to enhance the consistency between pairs of positive samples. Experimental results on four real-world datasets demonstrate the advantages of MixRec in terms of effectiveness, simplicity, efficiency, and scalability.
ACME++: A Secure Authorization Mechanism for ACME Clients in the Web PKI Ecosystem
Authors: Tianyu Zhang, Han Zhang, Yunze Wei, Yahui Li, Xingang Shi, Jilong Wang, Xia Yin
Venue: WWW
Year: 2025

The Automatic Certificate Management Environment (ACME) protocol automates the issuance and renewal of secure socket layer certificates, simplifying the management of large-scale certificate deployments. To reduce the load on Certificate Authority (CA) servers, ACME employs a caching mechanism that stores domain validation (DV) results for 30 days. However, this mechanism allows attackers to reuse previously authorized results, potentially bypassing the DV process. In this paper, we introduce the ACME Authz Cache Attack, whereby an attacker can obtain fraudulent certificates without domain control. We demonstrate that even the prominent CA, Let's Encrypt, is vulnerable to this attack. To mitigate this, we propose ACME++, an enhanced protocol that binds the client's IP address and a unique identifier to the ACME account, ensuring secure authorization for each new client and effectively preventing the ACME Authz Cache Attack. Our implementation of ACME++ shows that it introduces little overhead to the CA server.
SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant
Authors: Yifan Zhang, Xinkui Zhao, Zuxin Wang, Zhengyi Zhou, Guanjie Cheng, Shuiguang Deng, Jianwei Yin
Venue: WWW
Year: 2025

Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments. Traditional teaching methods often fail to address the diverse backgrounds, learning speeds, and practical needs of students. To tackle these challenges, we present SortingHat, a personalized digital teaching assistant tailored specifically for OS education. SortingHat integrates advanced AI technologies, including a retrieval-augmented generation (RAG) framework and multi-agent reinforcement learning (MARL), to deliver adaptive, scalable, and effective educational support. SortingHat features a 3D digital human interface powered by large language models (LLMs) to provide personalized, empathetic, and context-aware guidance. It generates tailored exercises based on each student's learning history and academic performance, reinforcing weak areas and challenging advanced concepts. Additionally, the system incorporates a robust evaluation pipeline that ensures fair, consistent, and unbiased grading of student submissions while delivering personalized, actionable feedback for improvement. By combining personalized guidance, adaptive content creation, and automated assessment, SortingHat transforms OS education into an engaging, immersive, and scalable experience.
Dual Operation Aggregation Graph Neural Networks for Solving Flexible Job-Shop Scheduling Problem with Reinforcement Learning
Authors: Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang
Venue: WWW
Year: 2025

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods fail to learn relationships between FJSP nodes, such as interactions between operations of different jobs, leading to limited interpretability and performance. To address these issues, we propose a dual operation aggregation graph neural network (GNN) for solving FJSP. Specifically, we decouple the disjunctive graph into two distinct graphs, reducing graph density and clarifying relationships between machines and operations, thus enabling more effective aggregation and understanding by neural networks. We develop two distinct graph aggregation methods to minimize the influence of non-critical machine and operation nodes on decision-making while enhancing the model's ability to account for long-term benefits. Additionally, to achieve more accurate multi-objective estimation and mitigate reward sparsity, we design a reward function that simultaneously considers machine efficiency, schedule balance, and makespan minimization. Extensive experimental results on well-known datasets demonstrate that our model outperforms state-of-the-art models and exhibits excellent generalization capabilities, effectively addressing the challenges of cloud manufacturing.