OpenRaiser/Intern-Atlas huggingface dataset:
Boosting Graph Convolution with Disparity-induced Structural Refinement
Authors: Sujia Huang, Yueyang Pi, Tong Zhang, Wenzhe Liu, Zhen Cui
Venue: WWW
Year: 2025

Graph Neural Networks (GNNs) have expressed remarkable capability in processing graph-structured data. Recent studies have found that most GNNs rely on the homophily assumption of graphs, leading to unsatisfactory performance on heterophilous graphs. While certain methods have been developed to address heterophilous links, they lack more precise estimation of high-order relationships between nodes. This could result in the aggregation of excessive interference information during message propagation, thus degrading the representation ability of learned features. In this work, we propose a Disparity-induced Structural Refinement (DSR) method that enables adaptive and selective message propagation in GNN, to enhance representation learning in heterophilous graphs. We theoretically analyze the necessity of structural refinement during message passing grounded in the derivation of error bound for node classification. To this end, we design a disparity score that combines both features and structural information at the node level, reflecting the connectivity degree of hopping neighbor nodes. Based on the disparity score, we can adjust the aggregation of neighbor nodes, thereby mitigating the impact of irrelevant information during message passing. Experimental results demonstrate that our method achieves competitive performance, mostly outperforming advanced methods on both homophilous and heterophilous datasets.
Disentangling Scenario-wise Interest Network for Multi-scenario Recommendation
Authors: Jin Huang, Yu Qian, Zixu Yang, Zhijun Sun
Venue: WWW
Year: 2025

With rapid development of the E-commerce platforms, multiple shopping scenarios are rapidly emerged to cater to the diversified needs of users. Currently, numerous models have been proposed to use a unified framework to serve multiple scenarios. However, most of those methods aggregate historical behavior sequences as a whole when modeling users' interests, neglect the discrepancy of user behaviors in different scenarios. To accurately capture the migration of interests across different scenarios, we propose a novel Disentangling Scenario-wise Interest Network, which explicitly incorporates scenario related context into user behavior sequences and learns users' scenario-wise interests. DSWIN consists of three key modules: 1) Global Interest Aggregation (GIA), which fuses users' global behaviors and scenario-aware context information with useful aggregation mechanism; 2) Local Interest Resolution (LIR), which explicitly extracts users' scenario-wise interests in each scenario through self-attention; 3) Interest Disentangling Module (IDM), which disentangles and distinguishes users' interests in different scenarios with self-supervised learning. Extensive experimental results and online A/B testing validate the effectiveness of DSWIN compared with state-of-the-art methods.
Can Large Language Models Be a Good Evaluator for Review-based Product Question Answering?
Authors: Tony Danhui Huang, Yongli Ren, Xiuzhen Zhang
Venue: WWW
Year: 2025

Large language models (LLMs) have demonstrated impressive performance on evaluating natural language generation tasks. Review-based Product Question Answering (RPQA) evaluation, which is a domain knowledge-intensive Question Answering (QA) evaluation, still largely relies on lexicon-based metrics and frozen embedding-based metrics. Those metrics fail if reference answers are absent. Despite some model-based metrics being learned from in-domain data for the RPQA task evaluation, little research has been done on using LLMs. Chain-of-Thought (CoT) is a state-of-the-art prompting method that has been proposed to induce LLMs to solve complex problems efficiently. The CoT reasoning steps can be resolved using the internal knowledge of LLMs, but this internal knowledge may be insufficient in domain knowledge-intensive RPQA evaluation settings. In this work, we explore the feasibility of leveraging LLMs as evaluators for RPQA evaluation. Specifically, we design a structured prompt template with one-shot or few-shot in-context learning, incorporating the CoT mechanism and the System 2 thinking process for RPQA evaluation. We call it as ExampLe-Enhanced CoT prompting (ELECT). We conduct a comprehensive study on various LLMs, including evaluation-oriented and general-purpose LLMs. Experimental results show that LLM-based evaluators can effectively evaluate RPQA. Upon integrating ELECT prompting into the demonstration examples in the prompt, we observe an improvement in the performance of the LLM-based evaluator. Code is publicly available at https://github.com/tonydhuang/elect-prompting.
Exploiting Language Power for Time Series Forecasting with Exogenous Variables
Authors: Qihe Huang, Zhengyang Zhou, Kuo Yang, Yang Wang
Venue: WWW
Year: 2025

The World Wide Web thrives on intelligent services that depend heavily on accurate time series forecasting to navigate dynamic and evolving environments. Due to the partially-observed nature of real world, exclusively focusing on the target of interest, so-called endogenous variables, is insufficient for accurate forecasting, especially in web systems that are susceptible to external influences. Thus, utilizing exogenous variables to harness external information, i.e., forecasting with exogenous variable (FEV), is imperative. Nevertheless, as the external environment is complex and ever-evolving, inadequately capturing external influences can even lead to learning spurious correlations and invalid prediction. Fortunately, recent studies have demonstrated that large language models (LLMs) exhibit exceptional recognition capabilities across open real-world systems, including a deep understanding of exogenous environments. However, it is difficult to directly apply LLMs for FEV due to challenges of task activation, exogenous knowledge extraction, and feature space alignment. In this work, we devise ExoLLM, an LLM-driven method to sufficiently utilize Exogenous variables for time series forecasting. We begin by Meta-task Instruction to activate the knowledge transfer of LLM from natural language processing to FEV. To comprehensively understand the intricate and hierarchical influences of exogenous variables, we propose Multi-grained Prompts, encompassing diverse external influences, including natural attributes, trend correlations, and period relationships between two types of variables. Additionally, a Dual TS-Text Attention is devised to bridge the feature gap between text and numeric data in LLM. Evaluation on real-world datasets demonstrates ExoLLM's superiority in exploiting exogenous information for forecasting with open-world language knowledge.
RU-AI: A Large Multimodal Dataset for Machine-Generated Content Detection
Authors: Liting Huang, Zhihao Zhang, Yiran Zhang, Xiyue Zhou, Shoujin Wang
Venue: WWW
Year: 2025

The recent generative AI models' capability of creating realistic and human-like content is significantly transforming the ways in which people communicate, create and work. The machine-generated content is a double-edged sword. On one hand, it can benefit the society when used appropriately. On the other hand, it may mislead people, posing threats to the society, especially when mixed together with natural content created by humans. Hence, there is an urgent need to develop effective methods to detect machine-generated content. However, the lack of aligned multimodal datasets inhibited the development of such methods, particularly in triple-modality settings (e.g., text, image, and voice). In this paper, we introduce RU-AI, a new large-scale multimodal dataset for robust and effective detection of machine-generated content in text, image and voice. Our dataset is constructed on the basis of three large publicly available datasets: Flickr8K, COCO and Places205, by adding their corresponding AI duplicates, resulting in a total of 1,475,370 instances. In addition, we created an additional noise variant of the dataset for testing the robustness of detection models. We conducted extensive experiments with the current SOTA detection methods on our dataset. The results reveal that existing models still struggle to achieve accurate and robust detection on our dataset. We hope that this new data set can promote research in the field of machine-generated content detection, fostering the responsible use of generative AI. The source code and datasets are available at https://github.com/ZhihaoZhang97/RU-AI.
Content Filtering on YouTube: An LLM Approach for Detecting and Scoring Harmful Content
Authors: Syed Mahbubul Huq, Basem Suleiman
Venue: WWW
Year: 2025

With over 2.5 billion users, YouTube is one of the largest video-sharing platforms. Although filtering tools exist, there is still a need for more personalized and effective systems to detect harmful content for younger audiences. This paper proposes a new framework using Large Language Models (LLMs) to detect and score harmful content in video subtitles automatically. The system provides personalized insights for parents, improving transparency and allowing for quick, customized content evaluation. Our approach integrates LLMs for scalable content scoring on the presence of harmful elements in a video. We show how this framework improves content moderation for children and offers a scalable solution for YouTube. Unlike traditional methods, our approach provides personalized and dynamic filtering based on text, with plans to extend to multimodal analysis to detect harmful visual content.
FP-Rainbow: Fingerprint-Based Browser Configuration Identification
Authors: Maxime Huyghe, Walter Rudametkin, Clément Quinton
Venue: WWW
Year: 2025

Browser fingerprinting is a tracking technique that collects attributes and calls functions from the browser's APIs. Unlike cookies, browser fingerprints are difficult to evade or delete, raising significant privacy concerns for users as they can be used to re-identify individuals over browsing sessions without their consent. Yet, there has been limited research on the impact of browser configuration settings on these fingerprints. This paper introduces FP-Rainbow, a novel approach to systematically explore and map the configuration space of Chromium-based web browsers aiming to identify the impact of configuration parameters on browser fingerprints and their changes over time. We explore 1,748 configuration parameters (switches) and identify their impact on the browser's BOM (Browser Object Model). By collecting and analyzing over 61,000 fingerprints from 18 versions of Chromium, our study reveals that 32 to 56 of these configuration parameters (depending on versions), such as disable-3d-apis or disable-notifications, influence the fingerprint of a web browser. FP-Rainbow also proves efficient in identifying browser configuration parameters from unknown fingerprints, achieving an average successful identification rate of 84% when considering a single configuration parameter and 78% when multiple parameters are involved, across all evaluated browser versions. These findings emphasize the importance of measuring the impact of configuration parameters on browsers to develop safer and more privacy-friendly web browsers.
Serial Scammers and Attack of the Clones: How Scammers Coordinate Multiple Rug Pulls on Decentralized Exchanges
Authors: Phuong Duy Huynh, Son Hoang Dau, Nicholas Huppert, Joshua Cervenjak, Hoonie Sun, Hong Yen Tran, Xiaodong Li, Emanuele Viterbo
Venue: WWW
Year: 2025

We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around 384,000 scammer addresses behind all one-day Simple Rug Pulls on Uniswap (Ethereum) and Pancakeswap (BSC), and identified distinctive scam patterns including star, chain, and major (scam-funding) flow. These patterns, which collectively cover about 40% of all scammer addresses in our datasets, reveal typical ways scammers run multiple Rug Pulls and organize the money flow among different addresses. We then studied the more general concept of scam cluster, which comprises scammer addresses linked together via direct ETH/BNB transfers or behind the same scam pools. We found that scam token contracts are highly similar within each cluster (average similarities >70%) and dissimilar across different clusters (average similarities <30%), corroborating our view that each cluster belongs to the same scammer/scam organization. Lastly, we analyze the scam profit of individual scam pools and clusters, employing a novel cluster-aware profit formula that takes into account the important role of wash traders. The analysis shows that the existing formula inflates the profit by at least 35% on Uniswap and 24% on Pancakeswap.
Leveraging Large Language Models for Biomedical Knowledge Graph Construction and Querying: An Advanced NLP Approach
Authors: Suramya Jadhav, Suki Perumal, Yasmin Tadavi, Bikshita Dash, Srinivasan Parthiban
Venue: WWW
Year: 2025

This paper introduces a novel methodology for constructing a comprehensive biomedical knowledge graph by applying advanced Natural Language Processing (NLP) techniques. By leveraging Large Language Models (LLMs) and a multifaceted prompt engineering approach, we effectively perform Named Entity Recognition (NER) and Relation Extraction (RE) on biomedical literature, targeting entities such as diseases, drugs, proteins, procedures, and symptoms. Our methodology incorporates eight distinct prompt engineering strategies for NER and a standardized approach for RE, facilitating the extraction of intricate inter-entity relationships. The resulting knowledge graph amalgamates diverse data sources into a unified framework, enabling efficient querying, visualization, and analysis of biomedical information. Furthermore, we present an innovative query processing pipeline that integrates GPT-3.5 turbo with the knowledge graph, allowing users to interact with the graph through natural language. This integrated system empowers the discovery of novel correlations, accelerating scientific research and fostering interdisciplinary collaboration. This represents a substantial contribution to the field of biomedical knowledge graph construction, offering a robust platform for accelerating scientific discovery and informing clinical decision-making.
RealGraphOF: A High-Performance Graph Engine for Very Large Graph Analysis
Authors: Myung-Hwan Jang, Ikhyeon Jo, Duck-Ho Bae, Sang-Wook Kim
Venue: WWW
Year: 2025

Recently, single-machine-based graph engines, utilizing external storage within a single machine, have been studied extensively for efficient graph analysis. Existing studies, however, do not consider the situation where the graph data does not fit even the capacity of external storage, being stored in storages of multiple remote servers. In this case, loading parts of the graph along with transferring them over the network degrades the processing performance significantly. From this motivation, we propose RealGraphOF, an improved version of the original RealGraph, that processes large-scale real-world graphs efficiently by exploiting external storages in remote servers through NVMe-over-Fabrics. RealGraphOF employs (1) local storage caching to reduce expensive network transfers and (2) user-space/asynchronous IO to obtain higher IO bandwidth by issuing IO requests more frequently. Experimental results on real-world datasets show that RealGraphOF outperforms dramatically state-of-the-art graph engines including naive RealGraphOF.
Leveraging Refined Negative Feedback with LLM for Recommender Systems
Authors: Chanwoo Jeong, Yujin Kang, Yoon-Sik Cho
Venue: WWW
Year: 2025

Recently, there has been growing research on negative feedback in recommender systems. These studies use a fixed threshold to binarize feedback into positive or negative. However, such an approach bears limitations when the rating habits for expressing disappointment differ across users or when ratings are noisy. Motivated by the remarkable success of Large Language Models (LLMs), we investigate how LLM can address this challenge on the fly. To this end, we present ReFINe, Re surrecting F alsely I dentified Ne gative feedback with LLM. ReFINe classifies the negative feedback into two distinct types: Falsely identified negative with positive signals and Confirmed negative with only negative signals. To the best of our knowledge, our work is the first to propose and demonstrate the distinction between two perspectives on negative feedback. We first leverage LLM to better separate between positive and negative sets for each user, and implement Re-Weighted BPR, a dedicated Bayesian Personalized Ranking loss function tailored to our perspective on negative feedback. Experimental results show that our model outperforms strong baseline models. The code is available at https://github.com/Chanwoo-Jeong-2000/ReFINe.
Let the Augmentation Path Speak
Authors: Daomin Ji, Hui Luo, Zhifeng Bao, Shazia Sadiq, J. Shane Culpepper
Venue: WWW
Year: 2025

In this work, we introduce a novel tool AugPath, for discovering relevant tabular data from a data lake to augment a base table, thereby supporting a wide range of downstream tasks, such and training data augmentation and table question answering (Q&A). Unlike traditional methods, such as unionable and joinable table search, which rely on one-hop and direct relationships for table augmentation, AugPath focuses on augmentation paths-sequences of interconnected tables linked to the base table through both direct and indirect relationships. By exploring these multi-step connections, AugPath enables more comprehensive and informative data augmentations, uncovering insights that traditional methods might overlook. Specifically, given a base table and a natural language statement, AugPath aims to identify and rank augmentation paths based on three core components: (1) a User Intent Extractor that utilizes a large language model (LLM) to analyze user intent and convert natural language statements into specific and actionable data requirements; (2) a Candidate Path Retriever that identifies all possible augmentation paths within a data lake that can be used for augmenting the base table; and (3) a Path Utility Evaluator that scores and ranks the paths using a novel utility metric, which considers both their relevance to the query and the diversity of the data they provide. AugPath enables users to discover augmentation paths that enhance common downstream tasks, such as training data augmentation and single/multiple table Q&A.
Joint Modeling in Deep Recommender Systems
Authors: Pengyue Jia, Jingtong Gao, Yejing Wang, Yuhao Wang, Xiaopeng Li, Qidong Liu, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang
Venue: WWW
Year: 2025

In the current digital era, Deep Recommender Systems (DRS) are essential for navigating and tailoring online content to individual preferences. However, conventional approaches that rely primarily on a single recommendation task, scenario, data modality, or user behavior are increasingly inadequate for capturing users' complex and evolving preferences. This limitation highlights the need for joint modeling approaches that integrate multiple tasks, scenarios, modalities, and behaviors within the recommendation process, enhancing recommendation precision, efficiency, and personalization. In this tutorial, we aim to give a comprehensive survey on the recent progress of the joint modeling methods in recommendations, which includes multi-task, multi-scenario, multi-modal, and multi-behavior modeling. This work will provide academic researchers and industry professionals with a thorough understanding and clear insight into these areas, sparking new ideas, fostering discussions, and driving technological advancements in the field of deep recommender systems. Our tutorial homepage is released online https://applied-machine-learning-lab.github.io/Joint-Modeling-in-Deep-Recommender-Systems-WWW2025/.
StructRAG: Structure-Aware RAG Framework with Scholarly Knowledge Graph for Diverse Question Answering
Authors: Runsong Jia, Bowen Zhang, Sergio José Rodríguez Méndez, Pouya Ghiasnezhad Omran
Venue: WWW
Year: 2025

Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have shown promise in academic question answering. However, existing approaches often fail to fully utilize document structural information and lack diversity in retrieved contexts. This paper presents StructRAG, a structure-aware RAG framework that leverages scholarly knowledge graphs for enhanced question answering. Our framework features three key innovations: (1) an automated knowledge graph construction pipeline based on Deep Document Model (DDM) that preserves document hierarchical structure, (2) a structure-aware retrieval mechanism that combines semantic relevance with source diversity, and (3) a context-enhanced generation approach that integrates structural metadata for improved answer synthesis. Experimental results on 329 computer science papers demonstrate that StructRAG significantly outperforms vanilla RAG baseline. While maintaining comparable semantic accuracy (91% vs 90%), our approach achieves substantially higher diversity in generated answers (Distinct-1: 62% vs 52%, Distinct-2: 89% vs 78%) and better answer quality across all metrics, with notable improvements in relevance (29%) and readability (36.5%). These results demonstrate that StructRAG effectively enhances both the diversity and quality of academic question answering.
Pre-train and Fine-tune: Recommenders as Large Models
Authors: Zhenhao Jiang, Chenghao Chen, Hao Feng, Yu Yang, Jin Liu, Jie Zhang, Jia Jia, Ning Hu
Venue: WWW
Year: 2025

In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommenders. Existing multi-domain learning can alleviate this problem. However, the structure of the industrial recommendation system is complex, the amount of data is huge, and the training cost is extremely high, so it is difficult to modify the structure of the industrial recommender and re-train it. To fill this gap, we consider recommenders as large pre-trained models and fine-tune them. We first propose the theory of the information bottleneck for fine-tuning and present an explanation for the fine-tuning technique in recommenders. To tailor for recommendation, we design an information-aware adaptive kernel (IAK) technique to fine-tune the pre-trained recommender. Specifically, we define fine-tuning as two phases: knowledge compression and knowledge matching and let the training stage of IAK explicitly approximate these two phases. Our proposed approach designed from the essence of fine-tuning is well interpretable. Extensive online and offline experiments show the superiority of our proposed method. Besides, we also share unique and important lessons we learned when deploying the method in a large-scale online platform. We also present the potential issues of fine-tuning techniques in recommendation systems and the corresponding solutions. The recommender with IAK technique has been deployed on the homepage of a billion-scale online food platform for several months and has yielded considerable profits in our business.