OpenRaiser/Intern-Atlas huggingface dataset:
Towards Better Evaluation of Recommendation Algorithms with Bi-directional Item Response Theory
Authors: Ziqi Xu, Chenglong Ma, Yongli Ren, Jeffrey Chan, Wei Shao, Feng Xia
Venue: WWW
Year: 2025

Recommendation algorithms are typically evaluated on various datasets and compared against other algorithms employing diverse strategies. However, current evaluation practices predominantly rely on rank-based metrics, focusing solely on performance outcomes while overlooking the latent traits of datasets and recommendation algorithms. In this paper, we propose a bi-directional Item Response Theory (Bi-ReIRT) framework, which offers a fine-grained evaluation by simultaneously modelling the latent traits of recommendation algorithms (i.e., their ability) and datasets (i.e., their inherent challenges). This is the first work to apply the IRT framework for evaluating recommendation algorithms on the dataset level. The Bi-ReIRT framework enables visualisations of algorithms' performance across datasets with varying levels of inherent challenge. We conduct extensive experiments across a portfolio of recommendation algorithms and datasets, exploring the implications of key IRT parameters such as discrimination, difficulty, and ability. Moreover, the interpretability of these parameters provides deeper insights into the characteristics of both recommendation algorithms and datasets.
Quantitative Runtime Monitoring of Ethereum Transaction Attacks
Authors: Xinyao Xu, Ziyu Mao, Jianzhong Su, Xingwei Lin, David A. Basin, Jun Sun, Jingyi Wang
Venue: WWW
Year: 2025

The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks. Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types. To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum. MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks. We integrate a novel semantic lifting approach that extracts system behaviors relevant for various attacks, which can be analyzed using the monitoring tool MonPoly. Furthermore, we also equip MoE with quantitative capabilities to evaluate the similarity between a transaction and an attack formula to enhance its performance in identifying attacks, including near-miss attacks. We carry out extensive experiments with MoE on a labeled benchmark and a large-scale dataset containing over one million transactions. On the labeled benchmark, MoE successfully detects 92.0% attacks and achieves a 45.0% higher recall rate than competing state-of-the-art tool. MoE finds 3,319 attacks with 95.4% precision on the large dataset. Furthermore, MoE uses quantitative analysis to uncover 8% additional attacks. Finally, the average time for monitoring a transaction is less than 23 ms, positioning MoE as a promising practical solution for real-time attack detection for Ethereum.
Compress and Mix: Advancing Efficient Taxonomy Completion with Large Language Models
Authors: Hongyuan Xu, Yuhang Niu, Yanlong Wen, Xiaojie Yuan
Venue: WWW
Year: 2025

Taxonomy completion aims to integrate new concepts into existing taxonomies by determining their appropriate hypernym and hyponym. While semantic and structural information are crucial for this task, existing approaches often struggle to balance these aspects effectively. In this paper, we propose COMI, an efficient taxonomy completion framework that leverages large language models (LLMs) to capture both semantic and structural information in a unified manner. COMI compresses node semantics into token representations, enabling LLMs to efficiently process the input structure composed of these tokens. To enhance the model's understanding of the structure, a further fine-tuning process using contrastive learning with mixup data augmentation is applied, where mixup generates diverse and challenging negative samples. Through these innovations, COMI improves the integration of semantic and structural information, leading to more accurate taxonomy completion. The experimental results on three real-world datasets demonstrate that COMI achieves state-of-the-art performance while showing up to 284x faster inference compared to the previous best method. Our code and compressed tokens are available at https://github.com/cyclexu/COMI.
MAQInstruct: Instruction-based Unified Event Relation Extraction
Authors: Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou
Venue: WWW
Year: 2025

Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching. Recent advancements in large language models have shown impressive performance through instruction tuning. Nevertheless, in the task of event relation extraction, instruction-based methods face several challenges: there are a vast number of inference samples, and the relations between events are non-sequential. To tackle these challenges, we present an improved instruction-based event relation extraction framework named MAQInstruct. Firstly, we transform the task from extracting event relations using given event-event instructions to selecting events using given event-relation instructions, which reduces the number of samples required for inference. Then, by incorporating a bipartite matching loss, we reduce the dependency of the instruction-based method on the generation sequence. Our experimental results demonstrate that MAQInstruct significantly improves the performance of event relation extraction across multiple LLMs.
Enhancing Real Estate Valuation through Deep Modeling of Spatiotemporal Dependencies and Feature Interactions
Authors: Lin Xu, Yuankai Wu
Venue: WWW
Year: 2025

Real estate valuation is crucial for both economic stability and societal development. Accurate property valuations are essential in guiding individual decisions and macroeconomic policies, especially in the context of rapid urbanization. Recent web platforms have transformed real estate markets by using data-driven models to predict property values. Despite these advancements, challenges remain in modeling the complex interactions between continuous and categorical factors and in addressing the spatio-temporal dependencies inherent in real estate transactions. To address these challenges, we propose TabularCNP (Tabular Conditional Neural Process), a novel framework that leverages both deep learning models on tabular data and Conditional Neural Processes (CNPs). The framework incorporates deep tabular models to handle complex feature interactions, while conditioning the target property's value on nearby recent sales to capture spatiotemporal dependencies. By transforming real estate valuation into a tabular data-driven machine learning task, we effectively consider the intricate interactions between features. Additionally, we introduce a CNP- -based event logging module to capture the influence of nearby transactions. Extensive experiments on real-world datasets validate the proposed framework, demonstrating significant improvements in prediction accuracy by effectively modeling multifactor interactions and spatiotemporal dynamics. The code is available at https://anonymous.4open.science/r/TabularCNP-EE04/.
Workshop on Sustainable AI for the Future Web
Authors: Chang Xu, Yunke Wang, Jianyuan Guo, Daochang Liu, Minjing Dong, Yasmeen M. George, Johan Barthelemy, Yan Liu, Ling Chen
Venue: WWW
Year: 2025

AI has been widely integrated into web applications, and has revolutionized user experiences by enabling personalized services, optimizing decision-making and enhancing productivity. However, AI for the web application also poses significant sustainability challenges. AI models rely heavily on vast amounts of web data, consuming substantial computational resources and energy, while the proliferation of AI-generated content risks polluting the web with low-quality information and misinformation. This degradation of data quality threatens the robustness, fairness, and trustworthiness of AI systems, raising concerns about security, bias, and equitable outcomes across diverse user groups. Therefore, we propose this workshop that aims to advance research in sustainable AI for the web, focusing on energy-efficient, socially equitable, and technically robust AI models. By fostering interdisciplinary collaboration among AI, environmental science and social sciences experts, the workshop seeks to develop innovative solutions that ensure AI's long-term positive impact on the web.
HRFT: Mining High-Frequency Risk Factor Collections End-to-End via Transformer
Authors: Wenyan Xu, Rundong Wang, Chen Li, Yonghong Hu, Zhonghua Lu
Venue: WWW
Year: 2025

In quantitative trading, transforming historical stock data into interpretable, formulaic risk factors enhances the identification of market volatility and risk. Despite recent advancements in neural networks for extracting latent risk factors, these models remain limited to feature extraction and lack explicit, formulaic risk factor designs. By viewing symbolic mathematics as a language-where valid mathematical expressions serve as meaningful "sentences"-we propose framing the task of mining formulaic risk factors as a language modeling problem. In this paper, we introduce an end-to-end methodology, Intraday Risk Factor Transformer (IRFT), to directly generate complete formulaic risk factors, including constants. We use a hybrid symbolic-numeric vocabulary where symbolic tokens represent operators and stock features, and numeric tokens represent constants. We train a Transformer model on high-frequency trading (HFT) datasets to generate risk factors without relying on a predefined skeleton of operators (e.g., +, x, /, √x, log x, cos x). It determines the general form of the stock volatility law, including constants; for example, f(x) = tan(ax + b), where x is the stock price. We refine the predicted constants (a, b) using the Broyden--Fletcher--Goldfarb--Shanno (BFGS) algorithm to mitigate non-linear issues. Compared to the ten approaches in SRBench, an active benchmark for symbolic regression (SR), IRFT achieves a 30% higher investment return on the HS300 and S&P500 datasets, while achieving inference times that are orders of magnitude faster than existing methods in HF risk factor mining tasks. Our code and dataset are publicly accessible via the following GitHub repository: https://github.com/wencyxu/IRF-LLM-accepted-at-WWW25-
Winning Brick by Brick with Daily Slices: A 94-Task Unified XGBoost Solution for Brick Schema Classification
Authors: Meilan Xu, Zheng Wen, Xiuqing Xiang, Wenjie Fu, Bing Wang
Venue: WWW
Year: 2025

This paper introduces a unified solution for multilabel time-series classification in the Brick by Brick Challenge 2024, addressing the automation of building data standardization using IoT sensor streams. We propose a method combining feature engineering, label hierarchy compliance, and novel probability calibration to classify 94 Brick sub-classes from irregularly sampled data. The raw sensor data is initially segmented into daily intervals, followed by multi-domain feature engineering across four categories: statistical, temporal, spectral, and peak characteristics, yielding a comprehensive set of 78 engineered features. The multilabel task is transformed into a 91-class problem via label concatenation (-1/0/1). A key innovation is hierarchical probability calibration using a 64th-root transformation and aggregated averaging, suppressing extreme probabilities while preserving label dependencies. Our XGBoost model demonstrates consistent performance across evaluation sets, achieving macro F1 scores of 0.7366-0.7391 through stratified five-fold cross-validation on the validation set. The model attained a macro F1 score of 0.6127 on the public leaderboard, with an overall score of 0.544 when evaluated across both public and private test sets. Temporal difference features prove most discriminative. The calibration strategy enhances robustness against segmentation artifacts, aligning with competition metrics through prediction remapping (-1→0.1, 1→0.9). The solution's computational efficiency (14-hour GPU training and inference) and interpretable features enable scalable, real-world building energy management, advancing fine-grained equipment status classification for sustainable operations.
RLG-RAG: Guiding the Knowledge Retrieval and Evaluation in Retrieval-Augmented Generation Framework by Reasoning Logic
Authors: Kehan Xu, Kun Zhang, Wei Huang, Jingyuan Li, Yuanzhuo Wang
Venue: WWW
Year: 2025

The knowledge retrieval, integration, and evaluation processes in the RAG method lack the guidance of reasoning logic, leading to ongoing challenges in maintaining factual consistency. To address these issues, this paper proposes the RLG-RAG framework, which constructs a reasoning graph based on user queries to guide the knowledge retrieval, integration, and evaluation processes. By fully representing the reasoning logic of RAG, RLG-RAG dynamically models and integrates knowledge relationships during retrieval and defines a precise scope of relevant knowledge through sufficiency evaluation. This reduces inference-irrelevant knowledge that large language models may obtain. Experimental analyses on accuracy, factual consistency, and robustness demonstrate that RLG-RAG resists interference and provides accurate, factually consistent answers. The project URL is https://doi.org/10.5281/zenodo.14852250.
Tutorial on Landing Generative AI in Industrial Social and E-commerce Recsys
Authors: Da Xu, Danqing Zhang, Chuanwei Ruan, Lingling Zheng, Bo Yang, Guangyu Yang, Shuyuan Xu, Haixun Wang
Venue: WWW
Year: 2025

Over the past two years, generative AI (GenAI) has evolved rapidly, influencing interdisciplinary fields including social and e-commerce Recsys. Despite several exciting research advances, landing GenAI innovations in real-world Recsys remains challenging due to the sophistication of modern industrial product and systems. Our tutorial begins with a brief overview of industrial Recsys and GenAI fundamentals (including LLMOps), followed by the ongoing efforts and opportunities to enhance existing Recsys data and model with foundation models. We then explore how GenAI's curation and reasoning capabilities can be integrated into Recsys-for example, by repurposing raw content, incorporating external knowledge for display and creative optimization, and generating personalized insights/explanations to foster transparency and trust. Following this, the tutorial highlights how AI agents can reshape Recsys by introducing interactive reasoning and action loops, moving beyond traditional passive feedback models. Lastly, we shed insights on real-world solutions for human-AI alignment and responsible GenAI practices. A key feature of the tutorial is the presentation of the holistic AI, Infrastructure, LLMOps, and Product roadmap, including evaluation and responsible AI practices, based on production solutions from LinkedIn, Amazon, Meta, TikTok, Microsoft, and Instacart. Real-world case studies and demos are further provided for illustration. While GenRecsys is still in its early stages, this tutorial provides valuable insights and practical strategies for the Recsys and GenAI communities, bridging scientific research and applied solutions in this novel and rapidly growing field.
Graph Neural Controlled Differential Equations For Collaborative Filtering
Authors: Ke Xu, Weizhi Zhang, Zihe Song, Yuanjie Zhu, Philip S. Yu
Venue: WWW
Year: 2025

Graph Convolution Networks (GCNs) are widely considered state-of-the-art for recommendation systems. Several studies in the field of recommendation systems have attempted to apply collaborative filtering (CF) within the Neural ODE framework. These studies follow the same idea as LightGCN, which either removes the weight matrix or employs a discrete weight matrix. However, we argue that weight control is critical for neural ODE-based methods. Weight plays a crucial role in creating tailored graph convolution for each node, and employing a fixed or discrete weight prevents adjustment over time within the ODE function. This rigidity in the graph convolution reduces its adaptability, consequently hindering the performance of recommendations. In this study, to create an optimal control for Neural ODE-based recommendation, we introduce a new method called Graph Neural Controlled Differential Equations for Collaborative Filtering (CDE-CF). Our method improves the performance of the Graph ODE-based method by incorporating weight control in a continuous manner. To evaluate our approach, we conducted experiments on various datasets. The results show that our method surpasses competing baselines, including GCNs-based models and state-of-the-art Graph ODE-based methods.
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems
Authors: Zhenghai Xue, Qingpeng Cai, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An
Venue: WWW
Year: 2025

The field of Reinforcement Learning (RL) has garnered increasing attention for its ability of optimizing user retention in recommender systems. A primary obstacle in this optimization process is the environment non-stationarity stemming from the continual and complex evolution of user behavior patterns over time, such as variations in interaction rates and retention propensities. These changes pose significant challenges to existing RL algorithms for recommendations, leading to issues with dynamics and reward distribution shifts. This paper introduces a novel approach called Adaptive User Retention Optimization (AURO) to address this challenge. To navigate the recommendation policy in non-stationary environments, AURO introduces an state abstraction module in the policy network. The module is trained with a new value-based loss function, aligning its output with the estimated performance of the current policy. As the policy performance of RL is sensitive to environment drifts, the loss function enables the state abstraction to be reflective of environment changes and notify the recommendation policy to adapt accordingly. Additionally, the non-stationarity of the environment introduces the problem of implicit cold start, where the recommendation policy continuously interacts with users displaying novel behavior patterns. AURO encourages exploration guarded by performance-based rejection sampling to maintain a stable recommendation quality in the cost-sensitive online environment. Extensive empirical analysis are conducted in a user retention simulator, the MovieLens dataset, and a live short-video recommendation platform, demonstrating AURO's superior performance against all evaluated baseline algorithms. Code is available at https://github.com/AIDefender/AURO
Dual Intention Escape: Penetrating and Toxic Jailbreak Attack against Large Language Models
Authors: Yanni Xue, Jiakai Wang, Zixin Yin, Yuqing Ma, Haotong Qin, Renshuai Tao, Xianglong Liu
Venue: WWW
Year: 2025

Recently, the jailbreak attack, which generates adversarial prompts to bypass safety measures and mislead large language models (LLMs) to output harmful answers, has attracted extensive interest due to its potential to reveal the vulnerabilities of LLMs. However, ignoring the exploitation of the characteristics in intention understanding, existing studies could only generate prompts with weak attacking ability, failing to evade defenses (e.g., sensitive word detect) and causing malice(e.g., harmful outputs). Motivated by the mechanism in the psychology of human misjudgment, we propose a dual intention escape (DIE) jailbreak attack framework to generate more stealthy and toxic prompts to deceive LLMs to output harmful content. For stealthiness, inspired by the anchoring effect, we designed the Intention-anchored Malicious Concealment(IMC) module that hides the harmful intention behind a generated anchor intention by the recursive decomposition block and contrary intention nesting block. Since the anchor intention will be received first, the LLMs might pay less attention to the harmful intention and enter response status. For toxicity, we propose the Intention-reinforced Malicious Inducement (IMI) module based on the availability bias mechanism in a progressive malicious prompting approach. Due to the ongoing emergence of statements correlated to harmful intentions, the output content of LLMs will be closer to these more accessible intentions, i.e., more toxic. We conducted extensive experiments under black-box settings, supporting that DIE could achieve 100% ASR-R and 92.9% ASR-G against GPT3.5-turbo.
Federated Intelligence in Web: A Tutorial
Authors: Peng Yan, Shengchao Chen, Guodong Long
Venue: WWW
Year: 2025

The recent development of Web Intelligence has heightened privacy concerns among end-users. Federated intelligence offers a novel approach to restructuring Web Intelligence within a federated setting to better protect privacy. Additionally, the advent of large foundation models has notably enhanced the capability of individual agents to address complex problems and has reshaped the Web ecosystem. Numerous large language models and domain-specific foundation models underpin various applications, linking end-users to the connected Web. This tutorial presents federated intelligence as a strategy to develop a Web-based collective intelligence system by leveraging existing foundations and applications. In this framework, agents collaboratively enhance their intelligence by acquiring complementary knowledge and making fine-grained adaptations, enabling them to manage complex tasks across diverse web environments. The tutorial provides an in-depth review of recent advancements and potential directions in federated intelligence, detailing its core concepts, applications, and future developments. It is designed for scholars, practitioners, and interested audiences, offering a concise overview that facilitates quick understanding and encourages meaningful discussions on its future evolution.
Understanding and Detecting File Knowledge Leakage in GPT App Ecosystem
Authors: Chuan Yan, Bowei Guan, Yazhi Li, Mark Huasong Meng, Liuhuo Wan, Guangdong Bai
Venue: WWW
Year: 2025

OpenAI has enabled third-party developers to build applications around ChatGPT, known as GPTs, to expand its capability to handle complex and specialized tasks. A key feature of GPTs is Retrieval-Augmented Generation (RAG), which allows developers to upload documents containing domain knowledge or application context, referred to as file knowledge. However, these documents often contain sensitive information, and the security mechanisms governing access control in GPTs remains an underexplored area. In this work, we present the first comprehensive study on file knowledge leakage within GPTs. We develop GPTs-Filtor, leveraging the unique characteristics of GPTs deployment, to perform an in-depth analysis and detection of file knowledge leakage at both user interaction (i.e., prompt) and network transmission levels. Applying GPTs-Filtor to 8,000 popular GPTs across eight different categories, we reveal widespread vulnerabilities in the current GPTs development and deployment model. We detect 618 cases of leakage among 1,331 GPTs that involve uploaded file knowledge, leading to the exfiltration of 3,645 file contents that contain highly-sensitive data such as internal bank audit transaction records. Our work underscores the pressing need for improved security practices in GPTs development and deployment, providing crucial insights for the secure development of this young but rapidly evolving ecosystem.