OpenRaiser/Intern-Atlas huggingface dataset:
Cross-View Isolated Sign Language Recognition with Graph
Authors: Xinyu Wang
Venue: WWW
Year: 2025

In the Cross-View Isolated Sign Language Recognition (CV-ISLR) Challenge, we propose a graph-based method for recognizing cross-view sign language. Our analysis reveals three key observations: (1) sign language recognition (SLR) requires greater emphasis on the connections between different parts of the human body; (2) relationships, such as those between facial expressions and hand gestures, are crucial for understanding the meaning of sign language; (3) spatiotemporal relationships, particularly between a signer's hands, exhibit consistency across viewpoints, which can aid recognition from new perspectives. Based on these findings, we model RGB and depth videos as graphs, where edges represent the relationships between different body parts and spatiotemporal features. To further enhance recognition performance, we adopt ensemble techniques that combine models trained on different modalities. Our method achieved a recognition accuracy of 33.97% on the RGB-D dataset and secured second place in the RGB-D track of the CV-ISLR challenge. This demonstrates the effectiveness of graph-based modeling for cross-view sign language recognition.
Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing
Authors: Hengzhi Wang, Haoran Chen, Minghe Ma, Laizhong Cui
Venue: WWW
Year: 2025

Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client's data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism.
Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
Authors: Hao Wang, Wei Guo, Luankang Zhang, Jin Yao Chin, Yufei Ye, Huifeng Guo, Yong Liu, Defu Lian, Ruiming Tang, Enhong Chen
Venue: WWW
Year: 2025

In the era of information overload, recommendation systems play a pivotal role in filtering data and delivering personalized content. Recent advancements in feature interaction and user behavior modeling have significantly enhanced the recall and ranking processes of these systems. With the rise of large language models (LLMs), new opportunities have emerged to further improve recommendation systems. This tutorial explores two primary approaches for integrating LLMs: LLMs-enhanced recommendations, which leverage the reasoning capabilities of general LLMs, and generative large recommendation models, which focus on scaling and sophistication. While the former has been extensively covered in existing literature, the latter remains underexplored. This tutorial aims to fill this gap by providing a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions. Key topics include data quality, scaling laws, user behavior mining, and efficiency in training and inference. By engaging with this tutorial, participants will gain insights into the latest developments and future opportunities in the field, aiding both academic research and practical applications. The timely nature of this exploration supports the rapid evolution of recommendation systems, offering valuable guidance for researchers and practitioners alike.
Distinctiveness Maximization in Datasets Assemblage
Authors: Tingting Wang, Shixun Huang, Zhifeng Bao, J. Shane Culpepper, Volkan Dedeoglu, Reza Arablouei
Venue: WWW
Year: 2025

In this paper, given a user's query set and budget, we aim to use the limited budget to help users assemble a set of datasets that can enrich a base dataset by introducing the maximum number of distinct tuples (i.e., maximizing distinctiveness). We prove this problem to be NP-hard. A greedy algorithm using exact distinctiveness computation attains an approximation ratio of (1-e-1 )/2, but it lacks efficiency and scalability due to its frequent computation of the exact distinctiveness marginal gain of any candidate dataset for selection. This requires scanning through every tuple in candidate datasets and thus is unaffordable in practice. To overcome this limitation, we propose an efficient machine learning (ML)-based method for estimating the distinctiveness marginal gain of any candidate dataset. This effectively eliminates the need to test each tuple individually. Estimating the distinctiveness marginal gain of a dataset involves estimating the number of distinct tuples in the tuple sets returned by each query in a query set across multiple datasets. This can be viewed as the cardinality estimation for a query set on a set of datasets, and the proposed method is the first to tackle this cardinality estimation problem. This is a significant advancement over prior methods that were limited to single-query cardinality estimation on a single dataset and struggled with identifying overlaps among tuple sets returned by each query in a query set across multiple datasets. Extensive experiments using five real-world data pools demonstrate that our algorithm, which utilizes ML-based distinctiveness estimation, outperforms all relevant baselines in effectiveness, efficiency, and scalability. A case study on two downstream ML tasks also highlights its potential to find datasets with more useful tuples to enhance the performance of ML tasks.
ConNEF: An Encryption Framework for Deep Neural Network IP Protection
Authors: Lanjun Wang, Mingwang Hu, Shixiong Xu, Mengbiao Zhao, Jianlong Chang
Venue: WWW
Year: 2025

In the era of Machine Learning as a Service (MLaaS), service providers upload pre-trained deep neural networks (DNNs) to the website of the MLaaS platform, allowing customers to purchase model usage rights. Due to the substantial amount of training data and computational resources consumed, pre-trained DNNs have become significant assets for service providers. To protect the intellectual property (IP) of DNNs, existing active authentication methods encrypt the models to prevent unauthorized usage. However, these methods face challenges such as insufficient generalization, high overhead, and weak robustness. This paper proposes a Confusion Neuron-based Encryption Framework (ConNEF), which encrypts DNN models with a small portion of confusion neurons. Extensive experiments demonstrate that ConNEF is compatible with various networks across vision and language tasks, and outperforms baseline methods in effectiveness, efficiency, and robustness.
MORE'25 Multimedia Object Re-ID: Advancements, Challenges, and Opportunities
Authors: Yaxiong Wang, Yunzhong Hou, Shuyu Yang, Zhedong Zheng, Zhun Zhong, Liang Zheng
Venue: WWW
Year: 2025

Object re-identification (or object re-id) has gained significant attention in recent years, fueled by the increasing demand for advanced video analysis and safety systems. In object re-id, a query can be of different modalities, such as an image, a video, or natural language, containing or describing the object of interest. This workshop aims to bring together researchers, practitioners, and enthusiasts interested in object re-id to delve into the latest advancements, challenges, and opportunities in this dynamic field. The workshop covers a spectrum of topics related to object re-id, including but not limited to deep metric learning, multi-view data generation, video-based object re-id, cross-domain object re-id, and real-world applications. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in object re-id technology.
Local Differentially Private Release of Infinite Streams With Temporal Relevance
Authors: Runze Wang, Jiahao Liu, Miao Hu, Yipeng Zhou, Di Wu
Venue: WWW
Year: 2025

The data stream generated by users on web applications is often collected using a local differential privacy (LDP) approach to ensure privacy. This approach offers rigorous theoretical guarantees and low computational overhead, albeit at the expense of data utility. Data utility encompasses both the value of individual data points and the temporal relevance that exists between them, but existing studies primarily focus on enhancing the former utility while neglecting the latter. Furthermore, the collected data often requires cleaning, and we have demonstrated through a case study that data stream lacking time relevance poses a significant risk to users' privacy during the cleaning process. In this paper, for the first time we present an online LDP publishing mechanism while preserving the inherent temporal relevance for the infinite stream, called the Sampling Period Perturbation Algorithm (SPPA). Specifically, we model the temporal relevance between data points as the Fourier interpolation function, resulting in a computational complexity reduction from O(n2) to O(n log n) when compared with the conventional Markov approach in the offline setting. To strike a better balance between privacy and utility, we add noise to the sampling period due to its minimal impact on sensitivity, which is analyzed by our novel concepts of (ε,τ)-temporal indistinguishability and (ε,w,τ)-event LDP. Through extensive experiments, SPPA exhibits superior performance in terms of both data utility and privacy preservation compared to the state-of-the-art baselines. In particular, when ε=1, compared with the state-of-the-art baseline, SPPA diminishes the MSE by up to 64.2%, and raises the event monitoring efficiency by up to 21.4%.
Multi-modal Negative Sampling for Recommendation with User Interest
Authors: Jianfang Wang, Menghao Liang, Anunobi Victor Chibueze
Venue: WWW
Year: 2025

Multi-modal Recommender Systems (MRSs) integrate various types of data, such as images, text, and videos, to deliver more accurate and personalized recommendations to users. However, traditional Multi-modal recommendation methods typically rely on random negative sampling strategies, overlooking the latent semantic relationships between items. Furthermore, these methods often suffer from the loss of user interest information during the denoising process. To address these challenges, this paper proposes a novel approach called Multi-modal Negative Sampling for Recommendation with User Interest (MNS-UI). To correct negative sample selection, MNS-UI introduces a dynamic negative sampler that incorporates Multi-modal information and allow for more precise capture of item semantic relationships. Additionally, we propose a reliable graph construction technique that preserves trusted edges in the user-item interaction graph, thereby mitigating the loss of user interest during the denoising process. Furthermore, we design a loss function to align Multi-modal features with item IDs, which significantly enhances the system's recommendation performance. Experimental results demonstrate that MNS-UI significantly outperforms state-of-the-art methods across three real-world datasets, with an average improvement of 2.4% in Recall@20, 6.7% in NDCG@20, and a reduction of 87.3% in the number of training epochs required for convergence.
Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter
Authors: Hongyu Wang, Ying Li, Ronghong Huang, Xianghang Mi
Venue: WWW
Year: 2025

In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. This study is made possible by multiple machine learning tools that are designed to detect and analyze Posts of Illicit Promotion (PIPs) as well as revealing their underlying promotion campaigns. Particularly, we observe that PIPs are prevalent on Twitter, along with extensive visibility on other three popular OSNs including YouTube, Facebook, and TikTok. For instance, applying our PIP hunter to the Twitter platform for 6 months has led to the discovery of 12 million distinct PIPs which are widely distributed in 5 major natural languages and 10 illicit categories, e.g., drugs, data leakage, gambling, and weapon sales. Along the discovery of PIPs are 580K Twitter accounts publishing PIPs as well as 37K distinct instant messaging accounts that are embedded in PIPs and serve as next hops of communication with prospective customers. Also, an arms race between Twitter and illicit promotion operators is also observed. Especially, 90% PIPs can survice the first two months since getting published on Twitter, which is likely due to the diverse evasion tactics adopted by miscreants to masquerade PIPs.
Off-policy Evaluation for Multiple Actions in the Presence of Unobserved Confounders
Authors: Haolin Wang, Lin Liu, Jiuyong Li, Ziqi Xu, Jixue Liu, Zehong Cao, Debo Cheng
Venue: WWW
Year: 2025

Off-policy evaluation (OPE) is a crucial problem in reinforcement learning (RL), where the goal is to estimate the long-term cumulative reward of a target policy using historical data generated by a potentially different behaviour policy. In many real-world applications, such as precision medicine and recommendation systems, unobserved confounders may influence the action, reward, and state transition dynamics, which leads to biased estimates if not properly addressed. While existing methods for handling unobserved confounders in OPE focus on single-action settings, they are less effective in multi-action scenarios commonly found in practical applications, where an agent can take multiple actions simultaneously. In this paper, we propose a novel auxiliary variable-aided method for OPE in multi-action settings with unobserved confounders. Our approach overcomes the limitations of traditional auxiliary variable methods for multi-action scenarios by requiring only a single auxiliary variable, relaxing the need for as many auxiliary variables as the actions. Through theoretical analysis, we prove that our method provides an unbiased estimation of the target policy value. Empirical evaluations demonstrate that our estimator achieves better performance compared to existing baseline methods, highlighting its effectiveness and reliability in addressing unobserved confounders in multi-action OPE settings.
Mining User Preferences from Online Reviews with the Genre-aware Personalized Neural Topic Model
Authors: Rui Wang, Jiahao Lu, Xincheng Lv, Shuyu Chang, Yansheng Wu, Yuanzhi Yao, Haiping Huang, Guozi Sun
Venue: WWW
Year: 2025

Customer-generated reviews on e-commerce websites often contain valuable insights into users' interests in product genres and provide a rich source for mining user preferences. However, most existing neural topic models tend to generate meaningless topics that share low correlations with product genres. Furthermore, they often fail to mine user preferences and discover personalized topic profiles due to the absence of explicit user modeling. To address these limitations, we propose a novel Genre-aware Personalized neural Topic Model (GPTM), which incorporates product genre information into the topic modeling process to ensure the relevance between mined topics and product genres. Moreover, it could produce a personalized topic profile for each user by performing user preference modeling. Extensive experimental results on three publicly available Amazon review corpora validate the effectiveness of the proposed GPTM in genre-aware topic modeling. Furthermore, GPTM surpasses state-of-the-art baselines in user preference mining and generates high-quality personalized topic profiles.
SAHSD: Enhancing Hate Speech Detection in LLM-Powered Web Applications via Sentiment Analysis and Few-Shot Learning
Authors: Yulong Wang, Hong Li, Ni Wei
Venue: WWW
Year: 2025

As large language models (LLMs) increasingly power web applications, including social networks, the challenge of moderating hate speech has become a critical concern for the Web. These LLM-powered applications, while offering near-human interaction capabilities, are vulnerable to harmful or biased content due to imperfect training data scraped from the Web. Current hate speech detection methods often struggle with limited annotated data, especially for real-time moderation on these platforms. This paper introduces Sentiment-Aided Hate Speech Detection (SAHSD), a novel approach designed to enhance hate speech detection specifically in LLM-powered web applications. By treating hate speech detection as a few-shot learning task, SAHSD utilizes sentiment analysis to refine pre-trained language models (LM) for improved accuracy in recognizing harmful content. SAHSD first employs publicly available sentiment datasets to train a sentiment analysis model, which is then fine-tuned by merging sentiment prompts with hate speech prompts, enabling efficient and accurate detection even with limited training samples. The effectiveness of SAHSD is demonstrated through experiments on widely used web-sourced datasets like SBIC and HateXplain. SAHSD achieves an exceptional F1-score of 0.99 with only 64 training samples and outperforms advanced techniques such as ToKen, MRP, and HARE, with significant improvements of 33% on SBIC and 95% on HateXplain. SAHSD surpasses GPT-4 in generalization performance across multiple datasets, showing an 8% improvement when trained on equal-sized samples. These results underscore SAHSD's potential to enhance content moderation in LLM-driven web platforms, contributing to a safer, more inclusive and accountable Web ecosystem.
Horizontal Federated Heterogeneous Graph Learning: A Multi-Scale Adaptive Solution to Data Distribution Challenges
Authors: Jia Wang, Yawen Li, Zhe Xue, Yingxia Shao, Zeli Guan, Wenling Li
Venue: WWW
Year: 2025

Federated heterogeneous graph learning, an extension of federated learning, effectively represents complex multidimensional relationships while maintaining data privacy. In horizontal federated heterogeneous graph learning, data from different parties often vary in topology and semantics, leading to sensitivity to distribution imbalances and increasing topological complexity. These differences hinder models from learning shared representations and cause instability during training. To address these challenges, this paper proposes a novel multi-scale adaptive horizontal federated heterogeneous graph learning method MAFedHGL. A random masking mechanism forces the model to infer missing connections. The model also captures multi-hop and multi-path connections using high-order topology mining, enhancing robustness against structural heterogeneity. Dynamic semantic consistency modeling uses a masking matrix to recover and integrate diverse node attributes, ensuring both global and local semantic consistency. Using clustering coefficients as aggregation weights enables clients with richer structural information to contribute more effectively to the global model, improving adaptability and performance across varying data distributions in horizontal federated heterogeneous graph learning. Extensive experiments on multiple public heterogeneous graph datasets validate that the proposed method outperforms state-of-the-art methods in both performance and robustness across various data distribution scenarios.
Linking Souls to Humans: Blockchain Accounts with Credible Anonymity for Web 3.0 Decentralized Identity
Authors: Taotao Wang, Zibin Lin, Shengli Zhang, Long Shi, Qing Yang, Boris Düdder
Venue: WWW
Year: 2025

A decentralized identity system that can provide users with self-sovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0. The account system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity. However, a disadvantage of such completely anonymous identity system is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain. In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities. This work proposes zkBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems. zkBID links souls (blockchain accounts) to humans (users' personhood credentials) in a one-to-one manner to truly reflect the social relationships and interactions between humans on the blockchain. zkBID conceals the one-to-one relationships between blockchain accounts and users' personhood credentials for privacy protection using zero-knowledge proofs and linkable ring signatures. Thus, with zkBID, the users' blockchain accounts are credibly anonymous. Importantly, zkBID is fully decentralized: all user-related data are generated by users and verified by smart contracts on the blockchain. We implemented zkBID and built a blockchain test network for evaluation purposes. Our tests demonstrate the effectiveness of zkBID and suggest proper ways to configure zkBID system parameters.
Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLM
Authors: Xin Wang, Haoyang Li, Zeyang Zhang, Wenwu Zhu
Venue: WWW
Year: 2025

Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution (I.D.) hypothesis, i.e., testing and training graph data are sampled from the identical distribution. However, this I.D. hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, several advanced graph machine learning techniques which go beyond the I.D. hypothesis, have made great progress and attracted ever-increasing attention from the research community. This tutorial is to disseminate and promote the recent research achievement on graph out-of-distribution adaptation, graph out-of-distribution generalization, and large language models for tackling distribution shifts, which are exciting and fast-growing research directions in the general field of machine learning and data mining. We will advocate novel, high-quality research findings, as well as innovative solutions to the challenging problems in graph machine learning under distribution shifts and the applications on graphs. This topic is at the core of the scope of The Web Conference, and is attractive to machine learning as well as data mining audience from both academia and industry.