OpenRaiser/Intern-Atlas huggingface dataset:
HALO: Half Life-Based Outdated Fact Filtering in Temporal Knowledge Graphs
Authors: Feng Ding, Tingting Wang, Yupeng Gao, Shuo Yu, Jing Ren, Feng Xia
Venue: WWW
Year: 2025

Outdated facts in temporal knowledge graphs (TKGs) result from exceeding the expiration date of facts, which negatively impact reasoning performance on TKGs. However, existing reasoning methods primarily focus on positive importance of historical facts, neglecting adverse effects of outdated facts. Besides, training on these outdated facts yields extra computational cost. To address these challenges, we propose an outdated fact filtering framework named HALO, which quantifies the temporal validity of historical facts by exploring the half-life theory to filter outdated facts in TKGs. HALO consists of three modules: the temporal fact attention module, the dynamic relation-aware encoder module, and the outdated fact filtering module. Firstly, the temporal fact attention module captures the evolution of historical facts over time to identify relevant facts. Secondly, the dynamic relation-aware encoder module is designed for efficiently predicting the half life of each fact. Finally, we construct a time decay function based on the half-life theory to quantify the temporal validity of facts and filter outdated facts. Experimental results show that HALO outperforms the state-of-the-art TKG reasoning methods on three public datasets, demonstrating its effectiveness in detecting and filtering outdated facts.
Transferable and Forecastable User Targeting Foundation Model
Authors: Bin Dou, Baokun Wang, Yun Zhu, Xiaotong Lin, Yike Xu, Xiaorui Huang, Yang Chen, Yun Liu, Shaoshuai Han, Yongchao Liu, Tianyi Zhang, Yu Cheng, Weiqiang Wang, Chuntao Hong
Venue: WWW
Year: 2025

User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital marketing. However, existing user targeting methods encounter two significant challenges: (i) Poor cross-domain and cross-scenario transferability and generalization, and (ii) Insufficient forecastability in real-world applications. These limitations hinder their applicability across diverse industrial scenarios. In this work, we propose FOUND, an industrial-grade, transferable, and forecastable user targeting foundation model. To enhance cross-domain transferability, our framework integrates heterogeneous multi-scenario user data, aligning them with one-sentence targeting demand inputs through contrastive pre-training. For improved forecastability, the text description of each user is derived based on anticipated future behaviors, while user representations are constructed from historical information. Experimental results demonstrate that our approach significantly outperforms existing baselines in cross-domain, real-world user targeting scenarios, showcasing the superior capabilities of FOUND. Moreover, our method has been successfully deployed on the Alipay platform and is widely utilized across various scenarios.
Optimize Quantization for Large Language Models via Progressive Training
Authors: Jiangcun Du, Renren Jin, Wuwei Huang, Wei Liu, Jian Luan, Deyi Xiong
Venue: WWW
Year: 2025

Quantization has played a significant role in enabling large language models to operate efficiently. Quantization-Aware Training (QAT) compensates for the loss incurred during the quantization process through training, and has demonstrated promising results. However, in the case of extremely low-bit quantization, such as 3 bits, the performance of QAT degrades significantly. In this paper, we delve into the challenges associated with data selection and training approach. Specifically, we have initially analyzed which type of data yields better results when applying ultra low-bit QAT to base or chat models. Building on this analysis, we further propose an iterative training approach that enhances the stability of model quantization at extremely low-bit configuration. Experimental evaluations demonstrate the effectiveness of the proposed method.
ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models
Authors: Linkang Du, Zheng Zhu, Min Chen, Zhou Su, Shouling Ji, Peng Cheng, Jiming Chen, Zhikun Zhang
Venue: WWW
Year: 2025

Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed images and have widespread applications in art and design. As such, amateur users can easily imitate professional-level paintings by collecting an artist's work and fine-tuning the model, leading to concerns about artworks' copyright infringement. To tackle these issues, previous studies either add visually imperceptible perturbation to the artwork to change its underlying styles (perturbation-based methods) or embed post-training detectable watermarks in the artwork (watermark-based methods). However, when the artwork or the model has been published online, i.e., modification to the original artwork or model retraining is not feasible, these strategies might not be viable. To this end, we propose a novel method for data-use auditing in the text-to-image generation model. The general idea of ArtistAuditor is to identify if a suspicious model has been finetuned using the artworks of specific artists by analyzing the features related to the style. Concretely, ArtistAuditor employs a style extractor to obtain the multi-granularity style representations and treats artworks as samplings of an artist's style. Then, ArtistAuditor queries a trained discriminator to gain the auditing decisions. The experimental results on six combinations of models and datasets show that ArtistAuditor can achieve high AUC values (> 0.937). By studying ArtistAuditor's transferability and core modules, we provide valuable insights into the practical implementation. Finally, we demonstrate the effectiveness of ArtistAuditor in real-world cases by an online platform Scenario. ArtistAuditor is open-sourced at https://github.com/Jozenn/ArtistAuditor.
Large Language Models as Narrative-Driven Recommenders
Authors: Lukas Eberhard, Thorsten Ruprechter, Denis Helic
Venue: WWW
Year: 2025

Narrative-driven recommenders aim to provide personalized suggestions for user requests expressed in free-form text such as ''I want to watch a thriller with a mind-bending story, like Shutter Island.'' Although large language models (LLMs) have been shown to excel in processing general natural language queries, their effectiveness for handling such recommendation requests remains relatively unexplored. To close this gap, we compare the performance of 38 open- and closed-source LLMs of various sizes, such as LLama 3.2 and GPT-4o, in a movie recommendation setting. For this, we utilize a gold-standard, crowdworker-annotated dataset of posts from reddit's movie suggestion community and employ various prompting strategies, including zero-shot, identity, and few-shot prompting. Our findings demonstrate the ability of LLMs to generate contextually relevant movie recommendations, significantly outperforming other state-of-the-art approaches, such as doc2vec. While we find that closed-source and large-parameterized models generally perform best, medium-sized open-source models remain competitive, being only slightly outperformed by their more computationally expensive counterparts. Furthermore, we observe no significant differences across prompting strategies for most models, underscoring the effectiveness of simple approaches such as zero-shot prompting for narrative-driven recommendations. Overall, this work offers valuable insights for recommender system researchers as well as practitioners aiming to integrate LLMs into real-world recommendation tools.
Enhancing Data Quality by Identifying Influential Nodes: Integrating Complementary Features with CoCoFISo
Authors: Shima Esfandiari
Venue: WWW
Year: 2025

With the growth in the usage of social networks, data quality improvement has become important to optimize and speed up various methodologies. One approach to achieve this aim is through the removal of unnecessary data. This paper discusses this issue by ranking nodes based on their potential for dissemination. Earlier approaches to detecting influential nodes have been either manual combinations of structural graph properties or MCDM (Multiple Criteria Decision Making) methods. Yet, there is still a need for a method with improved accuracy and resolution. This paper emphasizes the necessity of choosing complementary features and the optimal ways of combining them. To attain this objective, we employ five various graph structural properties, including K-Shell (KS) and PageRank (PR), that are complementary. This study is the first to employ CoCoFISo (Combined Compromise for Ideal Solution) in the Multi-Criteria Decision Making (MCDM) method to obtain the key nodes. The assessments are made regarding accuracy, correlation, computational complexity, and resolution on five real-world networks. The experimental results show that the proposed method outperforms 12 state-of-the-art methods, including HCURank and HGSM.
A Multi-Attribute Model to Identify Influential Nodes in Complex Networks: Addressing the K-Shell Limitations
Authors: Shima Esfandiari
Venue: WWW
Year: 2025

Determining the influence capability of different nodes in complex networks has become increasingly important. Its applications include preventing disease spread, controlling rumor propagation, and maximizing product sales. Various methods have been designed for this purpose, using the structural properties of the graph. However, these methods still lack high resolution and accuracy. This paper addresses the problems of core-like groups and low resolution of the k-shell. We combine the k-shell index and degree, and finally present a formula that determines the influence capability of each node. Evaluations are conducted using the Susceptible-Infected-Recovered (SIR) model on 7 real-world networks, comparing 10 methods. The results show that the proposed method has linear time complexity, achieves higher accuracy and resolution than benchmark methods.
What is the Chance of Being so Unfair?
Authors: Hossein Esfandiari, Samira Hossein Ghorban, Reza Hosseini Dolatabadi, Mahdi Qaempanah
Venue: WWW
Year: 2025

Fairness has often been seen as an ethical concern that needs to be considered at some cost on the utility. In contrast, in this work, we formulate fairness, and especially fairness in ranking, as a way to avoid unjust biases and provide a more accurate ranking that results in improvement on the actual unbiased utility. With this in mind, we design a fairness measure that, instead of blindly forcing some approximate equality constraint, checks if the outcome is plausible in a just world. Our fairness measure asks a simple and fundamental statistical question: ''What is the chance of observing this outcome in an unbiased world?''. If the chance is high enough, the outcome is fair. We provide a dynamic programming algorithm that, given a ranking, calculates our fairness measure. Secondly, given a sequence of potentially biased scores, along with the sensitive feature, we provide a fair ranking algorithm based on our fairness measure. Finally, we run some experiments to understand the behavior of our ranking algorithm against other fundamental algorithms.
MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network
Authors: Kai Fang, Jiangtao Deng, Chengzu Dong, Usman Naseem, Tongcun Liu, Hailin Feng, Wei Wang
Venue: WWW
Year: 2025

Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes. Additionally, MoCFL integrates historical and current feature information when training the global classifier, effectively mitigating the catastrophic forgetting problem frequently encountered in mobile scenarios. This synergistic combination ensures that MoCFL maintains high performance and stability in dynamically changing mobile environments. Experimental results on the UNSW-NB15 dataset show that MoCFL excels in dynamic environments, demonstrating superior robustness and accuracy while maintaining reasonable training costs.
EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering
Authors: Yuan Fang, Xiaofeng Feng, Geping Yang, Ruichu Cai, Yiyang Yang, Zhiguo Gong, Zhifeng Hao
Venue: WWW
Year: 2025

Contemporary datasets sourced from the web often adopt a multi-view format, collecting data from diverse sources, domains, or modules. Existing methodologies employed to analyze such datasets frequently overlook or inaccurately allocate the view-weights, pivotal metrics reflecting each view's significance. This work introduces EVA-MVC, a simple yet effective algorithm designed for Equitable View-weight Allocation (EVA) seamlessly integrated with arbitrary Multi-view Clustering (MVC) methods. Within the EVA module, we establish theoretical connections between view supplementarity and Multi-view Subspace Learning (MSL), leading to the partition of views into View Communities (VCs) based on these foundational principles. These VCs exhibit internal supplementarity similarities, facilitating Equitable View-weights Allocation through VC-specific MSL. The proposed EVA process precedes and operates independently of traditional or SOTA MVC approaches, requiring no additional processing or specialized design, making it an ideal preprocessing step for MVC applications. Through comprehensive evaluations across diverse multi-view datasets, our findings reveal that our EVA significantly enhances the effectiveness of mainstream MVC frameworks, resulting in a notable performance improvement.
Provably Robust Federated Reinforcement Learning
Authors: Minghong Fang, Xilong Wang, Neil Zhenqiang Gong
Venue: WWW
Year: 2025

Federated reinforcement learning (FRL) allows agents to jointly learn a global decision-making policy under the guidance of a central server. While FRL has advantages, its decentralized design makes it prone to poisoning attacks. To mitigate this, Byzantine-robust aggregation techniques tailored for FRL have been introduced. Yet, in our work, we reveal that these current Byzantine-robust techniques are not immune to our newly introduced Normalized attack. Distinct from previous attacks that targeted enlarging the distance of policy updates before and after an attack, our Normalized attack emphasizes on maximizing the angle of deviation between these updates. To counter these threats, we develop an ensemble FRL approach that is provably secure against both known and our newly proposed attacks. Our ensemble method involves training multiple global policies, where each is learnt by a group of agents using any foundational aggregation rule. These well-trained global policies then individually predict the action for a specific test state. The ultimate action is chosen based on a majority vote for discrete action systems or the geometric median for continuous ones. Our experimental results across different settings show that the Normalized attack can greatly disrupt non-ensemble Byzantine-robust methods, and our ensemble approach offers substantial resistance against poisoning attacks.
Few-Shot Learning on Graphs: From Meta-Learning to LLM-empowered Pre-Training and Beyond
Authors: Yuan Fang, Yuxia Wu, Xingtong Yu, Shirui Pan
Venue: WWW
Year: 2025

Graph representation learning has become central to many graph-based tasks, driving advancements in various domains such as web search, recommendation systems, and social network analysis. Traditionally, these methods rely on end-to-end supervised learning paradigms that require abundant labeled data, which can be costly and difficult to obtain. To address this limitation, few-shot learning on graphs has emerged as a promising approach, allowing models to generalize with minimal supervision and overcome data scarcity in real-world applications. This tutorial offers an in-depth exploration of recent advancements in few-shot learning for graphs, providing a comparative analysis of state-of-the-art methods and identifying future research directions. We categorize these approaches into two main taxonomies: (1) a problem taxonomy that examines various types of data scarcity problems and their applications, and (2) a technique taxonomy that outlines key strategies for tackling these challenges, including meta-learning, pre-training methods from both the pre-LLM and LLM eras. The tutorial will conclude by summarizing key insights from the literature and discussing future avenues for research, aiming to equip participants with a deep understanding of few-shot learning on graphs and inspire innovation in this rapidly growing field.
SOEX: Explainable AI for Competitor Communications Analytics
Authors: Aleksandr Farseev, Sergey I. Nikolenko, Qi Yang, Kirill Lepikhin, Da-Min You, Yu-Yi Chu-Farseeva, Ilia Gossoudarev, Marlo Ongpin
Venue: WWW
Year: 2025

In this demonstration, we present SOEX, an advanced AI system tailored for enhancing online marketing through the strategic use of large language models and predictive analytics. SOEX excels in analyzing vast amounts of online content data, enabling marketers to effectively plan and refine their strategies by extracting and utilizing key insights from competitor content and market trends. The system specializes in identifying content pillars-such as target audiences, consumer needs, and product features-and leveraging these pillars to generate detailed customer personas and narratives. By focusing on the initial stages of marketing, such as strategic planning and content pillar analysis, SOEX helps marketers understand and segment their audience more precisely. The system's ability to generate data-driven stories and personas from analyzed content enhances the planning phase of marketing campaigns, ensuring that strategies are both impactful and aligned with audience expectations. Our evaluations show that SOEX significantly aids marketers in navigating and interpreting complex data, leading to more informed decision-making and optimized marketing planning.
Leveraging Heterogeneous Spillover in Maximizing Contextual Bandit Rewards
Authors: Ahmed Sayeed Faruk, Elena Zheleva
Venue: WWW
Year: 2025

Recommender systems relying on contextual multi-armed bandits continuously improve relevant item recommendations by taking into account the contextual information. The objective of bandit algorithms is to learn the best arm (e.g., best item to recommend) for each user and thus maximize the cumulative rewards from user engagement with the recommendations. The context that these algorithms typically consider are the user and item attributes. However, in the context of social networks where the action of one user can influence the actions and rewards of other users, neighbors' actions are also a very important context, as they can have not only predictive power but also can impact future rewards through spillover. Moreover, influence susceptibility can vary for different people based on their preferences and the closeness of ties to other users which leads to heterogeneity in the spillover effects. Here, we present a framework that allows contextual multi-armed bandits to account for such heterogeneous spillovers when choosing the best arm for each user. Our experiments on several semi-synthetic and real-world datasets show that our framework leads to significantly higher rewards than existing state-of-the-art solutions that ignore the network information and potential spillover.
Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion
Authors: Giuseppe Fasano, Yashar Deldjoo, Tommaso Di Noia, Bianca Lau, Sina Adham-Khiabani, Eric Morris, Xia Liu, Ganga Chinna Rao Devarapu, Liam O&apos, Faolain
Venue: WWW
Year: 2025

This demo paper introduces AirSense-R, a privacy-preserving mobile application that delivers real-time, pollution-aware recommendations for urban points of interest (POIs). By merging live air quality data from AirSENCE sensor networks in Bari (Italy) and Cork (Ireland) with user preferences, the system enables health-conscious decision-making. It employs collaborative filtering for personalization, federated learning for privacy, and a prediction engine to detect anomalies and interpolate sparse sensor data. The proposed solution adapts dynamically to urban air quality while safeguarding user privacy. The code and demonstration video are available at https://github.com/AirtownApp/Airtown-Application.git.
Plug and Play: Enabling Pluggable Attribute Unlearning in Recommender Systems
Authors: Xiaohua Feng, Yuyuan Li, Fengyuan Yu, Li Zhang, Chaochao Chen, Xiaolin Zheng
Venue: WWW
Year: 2025

With the escalating privacy concerns in recommender systems, attribute unlearning has drawn widespread attention as an effective approach against attribute inference attacks. This approach focuses on unlearning users' privacy attributes to reduce the performance of attackers while preserving the overall effectiveness of recommendation. Current research attempts to achieve attribute unlearning through adversarial training and distribution alignment in the statistic setting. However, these methods often struggle in dynamic real-world environments, particularly when considering scenarios where unlearning requests are frequently updated. In this paper, we first identify three main challenges of current methods in dynamic environments, i.e., irreversible operation, low efficiency, and unsatisfied recommendation preservation. To overcome these challenges, we propose a Pluggable Attribute Unlearning framework, PAU. Upon receiving an unlearning request, PAU plugs an additional erasure module into the original model to achieve unlearning. This module can perform a reverse operation if the request is later withdrawn. To enhance the efficiency of unlearning, we introduce rate distortion theory and reduce the attack performance by maximizing the encoded bits required for users' embedding within the same class of the unlearned attribute and minimizing those for different classes, which eliminates the need to calculate the centroid distribution for alignment. We further preserve recommendation performance by constraining the compactness of the user embedding space around a reasonable flood level. Extensive experiments conducted on four real-world datasets and three mainstream recommendation models demonstrate the effectiveness of our proposed framework.
A Brief History of Images on the Web
Authors: Reinaldo Ferraz, Diogo Cortiz, Henrique S. Xavier
Venue: WWW
Year: 2025

This article explores the history of images on the Web, starting with a definition of 'image' and an overview of human-created images, from Stone Age paintings to portraits. It then presents essential milestones in developing digital images, such as the first digitized photograph, the evolution of digital cameras, image editor software, and image file formats. Finally, it explores the history of images on the Web, recounting the story of the first photograph on the network and the technological advancements leading up to the present day, including social media platforms for photo sharing and the emergence of AI-generated imagery. The article concludes with a brief discussion on the future of images on the Web.