OpenRaiser/Intern-Atlas huggingface dataset:
Facing Anomalies Head-On: Network Traffic Anomaly Detection via Uncertainty-Inspired Inter-Sample Differences
Authors: Xinglin Lian, Chengtai Cao, Yan Liu, Xovee Xu, Yu Zheng, Fan Zhou
Venue: WWW
Year: 2025

Network traffic anomaly detection is pivotal in cybersecurity, especially as data volume grows and security requirement intensifies. This study addresses critical limitations in existing reconstruction-based methods, which quantify anomalies relying on intra-sample differences and struggle to detect drifted anomalies. In response, we propose a novel approach, the Uncertainty-Inspired Inter-Sample Differences (UnDiff) method, which leverages model uncertainty to enhance anomaly detection capabilities, particularly in scenarios involving anomaly drift. By employing evidential learning, the UnDiff model gathers evidence to minimize uncertainty in normal network traffic, enhancing its ability to differentiate between normal and anomalous traffic. To overcome the limitations of intra-sample difference quantification in reconstruction-based methods, we propose a novel anomaly score based on inter-sample uncertainty deviation that directly quantifies the anomaly degree. Benefiting from a concise model design and parameterized uncertainty quantification, UnDiff achieves high efficiency. Extensive experiments on three benchmarks demonstrate UnDiff's superior performance in detecting both undrifted and drifted anomalies with minimal computational overhead.
EGRec: Leveraging Generative Rich Intents for Enhanced Recommendation with Large Language Models
Authors: Zhaorui Lian, Binzong Geng, Xiyu Chang, Yu Zhang, Ke Ding, Ziyu Lyu, Guanghu Yuan, Chengming Li, Min Yang, Zhaoxin Huan, Bin Shen, Yong He, Linjian Mo, Liang Zhang, Xing Zhu
Venue: WWW
Year: 2025

Recent advancements in Large Language Models (LLMs) leverage their extensive knowledge and reasoning to reduce semantic information loss in traditional Click-Through Rate (CTR) tasks. However, existing methods often neglect rich intent utilization, limiting the generative capabilities of LLMs. To address this, we propose an End-to-End Generative framework for learning rich Recommendation intents with LLMs, termed EGRec. EGRec employs a joint training paradigm for LLMs and CTR models, where LLMs generate rich user intents that are integrated into CTR models to enhance recommendation accuracy. By combining LLMs' semantic understanding with CTR models' predictive analytics, EGRec improves overall recommendation performance. Extensive experiments on the Amazon-Book dataset and proprietary Industrial-Ads data demonstrate EGRec's superiority over robust baseline methods.
TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding
Authors: Yuyang Liang, Yankai Chen, Yixiang Fang, Laks V. S. Lakshmanan, Chenhao Ma
Venue: WWW
Year: 2025

Electronic Health Records (EHR) have become a valuable resource for a wide range of predictive tasks in healthcare. However, existing approaches have largely focused on inter-visit event predictions, overlooking the importance of intra-visit nowcasting, which provides prompt clinical insights during an ongoing patient visit. To address this gap, we introduce the task of laboratory measurement prediction within a hospital visit. We study the laboratory data that, however, remained underexplored in previous work. We propose TRACE, a Transformer-based model designed for clinical event nowcasting by encoding patient trajectories. TRACE effectively handles long sequences and captures temporal dependencies through a novel timestamp embedding that integrates decay properties and periodic patterns of data. Additionally, we introduce a smoothed mask for denoising, improving the robustness of the model. Experiments on two large-scale electronic health record datasets demonstrate that the proposed model significantly outperforms previous methods, highlighting its potential for improving patient care through more accurate laboratory measurement nowcasting. The code is available at https://github.com/Amehi/TRACE.
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Authors: Mingfu Liang, Xi Liu, Rong Jin, Boyang Liu, Qiuling Suo, Qinghai Zhou, Song Zhou, Laming Chen, Hua Zheng, Zhiyuan Li, Shali Jiang, Jiyan Yang, Xiaozhen Xia, Fan Yang, Yasmine Badr, Ellie Wen, Shuyu Xu, Hansey Chen, Zhengyu Zhang, Jade Nie, Chunzhi Yang, Zhichen Zeng, Weilin Zhang, Xingliang Huang, Qianru Li, Shiquan Wang, Evelyn Lyu, Wenjing Lu, Rui Zhang, Wenjun Wang, Jason Rudy, Mengyue Hang, Kai Wang, Bo Long, Wenlin Chen, Santanu Kolay, Huayu Li
Venue: WWW
Year: 2025

Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a larger model scale, such prior studies have a significantly increasing gap from industry as they often neglect two fundamental challenges in industrial-scale applications. First, training and inference budgets are restricted for the model to be served, exceeding which may incur latency and impair user experience. Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system. We propose the External Large Foundation Model (ExFM) framework to address the overlooked challenges. Specifically, we develop external distillation and a data augmentation system (DAS) to control the computational cost of training/inference while maintaining high performance. We design the teacher in a way like a foundation model (FM) that can serve multiple students as vertical models (VMs) to amortize its building cost. We propose Auxiliary Head and Student Adapter to mitigate the data distribution gap between FM and VMs caused by the streaming data issue. Comprehensive experiments on internal industrial-scale applications and public datasets demonstrate significant performance gain by ExFM.
Damage Analysis via Bidirectional Multi-Task Cascaded Multimodal Fusion
Authors: Tao Liang, Siying Wu, Junfeng Fang, Guowu Yang, Wenya Wang, Fengmao Lv
Venue: WWW
Year: 2025

Damage analysis in social media platforms such as Twitter is a comprehensive problem which involves different subtasks for mining damage-related information from tweets ( e.g., informativeness, humanitarian categories and severity assessment). The comprehensive information obtained by damage analysis enables to identify breaking events around the world in real-time and hence provides aids in emergency responses. Recently, with the rapid development of web technologies, multimodal damage analysis has received increasing attentions due to users' preference of posting multimodal information in social media. Multimodal damage analysis leverages the associated image modality to improve the identification of damage-related information in social media. However, existing works on multimodal damage analysis address each damage-related subtask individually and do not consider their joint training mechanism. In this work, we propose the Bidirectional Multi-task Cascaded multimodal Fusion (BiMCF) approach towards joint multimodal damage analysis. To this end, we introduce the cascaded multimodal fusion framework to separately integrate effective visual and text information for each task, considering that different tasks attend to different information. To exploit the interactions across tasks, bidirectional propagation of the attended image-text interactive information is implemented between tasks, which can lead to enhanced multimodal fusion. Comprehensive experiments are conducted to validate the effectiveness of the proposed approach. Code is available at https://github.com/tiggers23/BiMCF.
The International Workshop on Spatio-Temporal Data Mining from the Web
Authors: Yuxuan Liang, Hao Xue, Ming Jin, Flora Salim, Qingsong Wen, Yong Li, Roger Zimmermann, Yu Zheng
Venue: WWW
Year: 2025

We propose a comprehensive full-day workshop (with at least 8 accepted papers, 4 keynote talks, and over 70 attendees) at WWW 2025, catering to professionals, researchers, and practitioners who are interested in sensing, mining, and understanding big and heterogeneous spatio-temporal data generated from the web (e.g., social media posts, geotagged images, mobility traces) to tackle real-world challenges, such as climate change, disaster response, urban planning, and location-based social networks. The workshop will not only offer a platform for knowledge exchange but also acknowledge outstanding contributions through a distinguished Best Paper Award. This workshop would be sponsored by JD Technology, with other committee members from MIT, CMU, UCB, and etc. Note that this will be the fourteenth time that our core members have organized a similar workshop. The previous 13 workshops were hosted with SIGKDD and SIGSPATIAL, each of which attracted over 70 participants and 30 submissions on average.
PSSD: Making Large Language Models Self-denial via Human Psyche Structure
Authors: Jinzhi Liao, Zenghua Liao, Xiang Zhao
Venue: WWW
Year: 2025

The enhance of accuracy in reasoning results of LLMs arouses the community's interests, wherein pioneering studies investigate post-hoc strategies to rectify potential mistakes. Despite extensive efforts, they are all stuck in a state of resource competition demand ing significant time and computing expenses. The cause of the situation lies in the failure of identifying the fundamental feature of the solutions in this line, coined as the self-denial of LLMs. In other words, LLMs should confidently determine the potential existence of mistakes and carefully execute the targeted correction. As the whole procedure conducts within LLMs, supporting and persuasive references are hard to acquire, while the absence of specific steps towards refining hidden mistakes persists even when errors are acknowledged. In response to the challenges, we present PSSD, which refers to and implements the human psyche structure such that three distinct and interconnected roles contribute to human reasoning. Specifically, PSSD leverages the recent multi-agent paradigm, and is further enhanced with three innovatively conceived roles: (1) the intuition-based id role that provides initial attempts based on benign LLMs; (2) the rule-driven superego role that summarizes rules to regulate the above attempts, and returns specific key points as guidance; and (3) the script-centric ego role that absorbs all procedural information to generate executable script for the final answer prediction. Extensive experiments demonstrate that the proposed design not only better enhance reasoning capabilities, but also seamlessly integrate with current models, leading to superior performance.
User Archetypes and Information Dynamics on Telegram: COVID-19 and Climate Change Discourse in Singapore
Authors: Val Alvern Cueco Ligo, Lam Yin Cheung, Roy Ka-Wei Lee, Koustuv Saha, Edson C. Tandoc Jr., Navin Kumar
Venue: WWW
Year: 2025

Social media platforms, particularly Telegram, play a pivotal role in shaping public perceptions and opinions on global and national issues. Unlike traditional news media, Telegram allows for the proliferation of user-generated content with minimal oversight, making it a significant venue for the spread of controversial and misinformative content. During the COVID-19 pandemic, Telegram's popularity surged in Singapore, a country with one of the highest rates of social media use globally. We leverage Singapore-based Telegram data to analyze information flows within groups focused on COVID-19 and climate change. Using k-means clustering, we identified distinct user archetypes, such as Strategic Disruptor, Empirical Enthusiast, Inquisitive Moderate, and Critical Examiner, each contributing uniquely to the discourse. We developed a model to classify users into these clusters (Precision: Climate change: 0.99; COVID-19: 0.95).
What's in Phishers: A Longitudinal Study of Security Configurations in Phishing Websites and Kits
Authors: Kyungchan Lim, Kiho Lee, Fujiao Ji, Yonghwi Kwon, Hyoungshick Kim, Doowon Kim
Venue: WWW
Year: 2025

Phishing attacks pose a significant threat to Internet users. Understanding the security posture of phishing infrastructure is crucial for developing effective defense strategies, as it helps identify potential weaknesses that attackers might exploit. Despite extensive research, there may still be a gap in fully understanding these security weaknesses. To address this important issue, this paper presents a longitudinal study of security configurations and vulnerabilities in phishing websites and associated kits. We focus on two main areas: (1) analyzing the security configurations of phishing websites and servers, particularly HTTP headers and application-level security, and (2) examining the prevalence and types of vulnerabilities in phishing kits. We analyze data from 906,731 distinct phishing websites collected over 2.5 years, covering HTML headers, client-side resources, and phishing kits. Our findings suggest that phishing websites often employ weak security configurations, with 88.8% of the 13,344 collected phishing kits containing at least one potential vulnerability, and 12.5% containing backdoor vulnerabilities. These vulnerabilities present an opportunity for defenders to shift from passive defense to active disruption of phishing operations. Our research proposes a new approach to leverage weaknesses in phishing infrastructure, allowing defenders to take proactive actions to disable phishing sites earlier and reduce their effectiveness.
CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization
Authors: Sida Lin, Yankai Chen, Yiyan Qi, Chenhao Ma, Bokai Cao, Yifei Zhang, Xue Liu, Jian Guo
Venue: WWW
Year: 2025

The stock market, as a cornerstone of the financial markets, places forecasting stock price movements at the forefront of challenges in quantitative finance. Emerging learning-based approaches have made significant progress in capturing the intricate and ever-evolving data patterns of modern markets. With the rapid expansion of the stock market, it presents two characteristics, i.e., stock exogeneity and volatility heterogeneity that heighten the complexity of price forecasting. Specifically, while stock exogeneity reflects the influence of external market factors on price movements, volatility heterogeneity showcases the varying difficulty in movement forecasting against price fluctuations. In this work, we introduce the framework of Cross-market Synergy with Pseudo-volatility Optimization (CSPO). Specifically, CSPO implements an effective deep neural architecture to leverage external futures knowledge. This enriches stock embeddings with cross-market insights and thus enhances the CSPO's predictive capability. Furthermore, CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence, enabling a dynamic adaptation of its optimization process to improve accuracy and robustness. Our extensive experiments, encompassing industrial evaluation and public benchmarking, highlight CSPO's superior performance over existing methods and effectiveness of all proposed modules contained therein.
Ask, Acquire, Understand: A Multimodal Agent-based Framework for Social Abuse Detection in Memes
Authors: Xuanrui Lin, Chao Jia, Junhui Ji, Hui Han, Usman Naseem
Venue: WWW
Year: 2025

Memes serve as a powerful medium of expression in the digital age, shaping cultural discourse and conveying ideas succinctly and engagingly. However, their potential for social abuse highlights the importance of developing effective methods to detect harmful content within memes. Recent studies on memes have focused on transforming images into textual captions using large language models (LLMs). However, these approaches often result in non-informative captions. Furthermore, previous methods have only been tested on limited datasets, providing insufficient evidence of their robustness. To address these limitations, we present a multimodal, agent-based framework designed to generate informative visual descriptions of memes by asking insightful questions to improve visual descriptions in zero-shot visual question-answering settings. Specifically, we leverage an LLM as agents with distinct roles and a large multimodal model (LMM) as a vision expert. These agents first analyze the images and then ask informative questions related to potential social abuse in memes to obtain high-quality answers about the images. Through continuous discussion guided by instructional prompts, the agents gather high-quality information while repeatedly acquiring image data from the LMM, which helps detect social abuse in memes. Results on a dataset of 6,626 memes across 5 tasks show our framework surpasses state-of-the-art methods, demonstrating strong generalizability and improved detection of social abuse in memes.
RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News Articles
Authors: Jonathan Lin, Aditya Joshi, Hye-young Paik, Tri Dung Duong, Deepti Gurdasani
Venue: WWW
Year: 2025

Geocoding involves automatic extraction of location coordinates of incidents reported in news articles, and can be used for epidemic intelligence or disaster management. This paper introduces Retrieval-Augmented Coordinate Capture Of Online News articles (RACCOON), an open-source geocoding approach that extracts geolocations from news articles. RACCOON uses a retrieval-augmented generation (RAG) approach where candidate locations and associated information are retrieved in the form of context from a location database, and a prompt containing the retrieved context, location mentions and news articles is fed to an LLM to generate the location coordinates. Our evaluation on three datasets, two underlying LLMs, three baselines and several ablation tests based on the components of RACCOON demonstrate the utility of RACCOON. To the best of our knowledge, RACCOON is the first RAG-based approach for geocoding using pre-trained LLMs.
BETag: Behavior-enhanced Item Tagging with Finetuned Large Language Models
Authors: Shao-En Lin, Brian Liu, Miao-Chen Chiang, Ming-Yi Hong, Yu-Shiang Huang, Chuan-Ju Wang, Che Lin
Venue: WWW
Year: 2025

Tags play a critical role in enhancing product discoverability, optimizing search results, and enriching recommendation systems on e-commerce platforms. Despite the recent advancements in large language models (LLMs), which have shown proficiency in processing and understanding textual information, their application in tag generation remains an under-explored yet complex challenge. To this end, we introduce a novel method for automatic product tagging using LLMs to create behavior-enhanced tags (BETags). Specifically, our approach begins by generating base tags using an LLM. These base tags are then refined into BETags by incorporating user behavior data. This method aligns the tags with users' actual browsing and purchasing behavior, enhancing the accuracy and relevance of tags to user preferences. By personalizing the base tags with user behavior data, BETags are able to capture deeper behavioral insights, which is essential for understanding nuanced user interests and preferences in e-commerce environments. Moreover, since BETags are generated offline, they do not impose real-time computational overhead and can be seamlessly integrated into downstream tasks commonly associated with recommendation systems and search optimization. Our evaluation of BETag across three datasets--- Amazon (Scientific), MovieLens-1M, and FreshFood---shows that our approach significantly outperforms both human-annotated tags and other automated methods. These results highlight BETag as a scalable and efficient solution for personalized automated tagging, advancing e-commerce platforms by creating more tailored and engaging user experiences.
White Admitted by Stanford, Black Got Rejections: Exploring Racial Stereotypes in Text-to-Image Generation from a College Admissions Lens
Authors: Yezi Liu, Wenjun Huang, Yang Ni, Hanning Chen, Mohsen Imani
Venue: WWW
Year: 2025

In this paper, we investigate racial stereotypes in T2I models through the lens of U.S. college admissions. Our findings reveal a significant bias in the generated images: T2I models are more likely to produce images of white students when positive prompts such as ''admitted'' are used, whereas images of black students are more likely to be generated with negative prompts such as ''rejected''. We further tested various college admission scenarios, including application outcomes (success/fail), college rankings (top-ranked/non-top-ranked), geographical regions, and the number of students in the generated images. We discovered the following patterns: (1) Overall, white individuals are generated most often in any scene (success/fail, single-person/group), and white males are predominantly generated in successful admission scenes. (2) Dall·E 3 is more likely to revise prompts to be more equitable (by adding descriptions to ensure an equivalent number of individuals from different races) when the original prompts concern top-ranked colleges, but it is less likely to do so for other colleges. (3) Asians are generated more frequently for top-ranked colleges. (4) In Southern college settings, white students form the majority in the generated images, while other races are underrepresented compared to the settings of other regions, such as the Midwest or the North. Overall, our study indicates that T2I models have harmful stereotypes: white males are commonly associated with success, black individuals are often associated with failure, and Asians are linked to intelligence and top-tier institutions. To address this, a simple, bias-free, and user-friendly solution is: when prompted to generate images of humans, the T2I models should present multiple options featuring different racial compositions, allowing users to select their preferred choice.
Uncovering Cross-Domain Recommendation Ability of Large Language Models
Authors: Xinyi Liu, Ruijie Wang, Dachun Sun, Dilek Hakkani-Tür, Tarek F. Abdelzaher
Venue: WWW
Year: 2025

Cross-Domain Recommendation (CDR) seeks to enhance item retrieval in low-resource domains by transferring knowledge from high-resource domains. While recent advancements in Large Language Models (LLMs) have demonstrated their potential in Recommender Systems (RS), their ability to effectively transfer domain knowledge for improved recommendations remains underexplored. To bridge this gap, we propose LLM4CDR, a novel CDR pipeline that constructs context-aware prompts by leveraging users' purchase history sequences from a source domain along with shared features between source and target domains. Through extensive experiments, we show that LLM4CDR achieves strong performance, particularly when using LLMs with large parameter sizes and when the source and target domains exhibit smaller domain gaps. For instance, incorporating CD & Vinyl purchase history for recommendations in Movies & TV yields a 64.28% MAP@1 improvement. We further investigate how key factors-source domain data, domain gap, prompt design, and LLM size-impact LLM4CDR's effectiveness in CDR tasks. Our results highlight that LLM4CDR excels when leveraging a single, closely related source domain and benefits significantly from larger LLMs. These insights pave the way for future research on LLM-driven cross-domain recommendations.