OpenRaiser/Intern-Atlas huggingface dataset:
Moral Values Underpinning COVID-19 Online Communication Patterns
Authors: Julie Jiang, Luca Luceri, Emilio Ferrara
Venue: WWW
Year: 2025

The COVID-19 pandemic has triggered profound societal changes, extending beyond its health impacts to the moralization of behaviors. Leveraging insights from moral psychology, this study delves into the moral fabric shaping online discussions surrounding COVID-19 over a span of nearly two years. Our investigation identifies four distinct user groups characterized by differences in morality, political ideology, and communication styles. We underscore the intricate relationship between moral differences and political ideologies, revealing a nuanced picture where moral orientations do not rigidly separate users politically. Furthermore, we uncover patterns of moral homophily within the social network, highlighting the existence of one potential moral echo chamber. Analyzing the moral themes embedded in messages, we observe that moral diversity and moral plurality resonate more effectively with out-group members. This research contributes valuable insights into the complex interplay between moral foundations, communication dynamics, and network structures on Twitter.
Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement
Authors: Hao Jiang, Yongxiang Tang, Yanxiang Zeng, Pengjia Yuan, Yanhua Cheng, Teng Sha, Xialong Liu, Peng Jiang
Venue: WWW
Year: 2025

In the realm of online advertising, advertisers partake in ad auctions to obtain advertising slots, frequently taking advantage of auto-bidding tools provided by demand-side platforms. To improve the automation of these bidding systems, we adopt generative models, namely the Decision Transformer (DT), to tackle the difficulties inherent in automated bidding. Applying the Decision Transformer to the auto-bidding task enables a unified approach to sequential modeling, which efficiently overcomes short-sightedness by capturing long-term dependencies between past bidding actions and user behavior. Nevertheless, conventional DT has certain drawbacks: (1) DT necessitates a preset return-to-go (RTG) value before generating actions, which is not inherently produced; (2) The policy learned by DT is restricted by its training data, which is consists of mixed-quality trajectories. To address these challenges, we introduce the R* Decision Transformer (R* DT), developed in a three-step process: (1) R DT: Similar to traditional DT, R DT stores actions based on state and RTG value, as well as memorizing the RTG for a given state using the training set; (2) R^ DT: We forecast the highest value (within the training set) of RTG for a given state, deriving a suboptimal policy based on the current state and the forecasted supreme RTG value; (3) R* DT: Based on R^ DT, we generate trajectories and select those with high rewards (using a simulator) to augment our training dataset. This data enhancement has been shown to improve the RTG of trajectories in the training data and gradually leads the suboptimal policy towards optimality. Comprehensive tests on a publicly available bidding dataset validate the R* DT's efficacy and highlight its superiority when dealing with mixed-quality trajectories.
DMLoRA: Dynamic Multi-Subspace Low-Rank Adaptation
Authors: Cong Jiang, Fangzhi Zhu, Xiaowei Chen, Junxiong Zhu, Bo Zheng, Yifeng Wang, Zheng Zhang
Venue: WWW
Year: 2025

As one of the most widely adopted parameter-efficient fine-tuning (PEFT) techniques, LoRA and its variants have garnered significant attention for their ability to avoid additional inference costs. However, the standard LoRA struggles to fully match the expressive capacity of fully fine-tuned models due to inherent approximation errors and the limited flexibility of rank-level component weights. In this work, we propose a novel Dynamic Multi-subspace LoRA (DMLoRA), which partitions high-dimensional input features into multiple subspaces, each optimized with dynamic rank-level weighting. By employing dynamic weights within fine-grained subspaces, DMLoRA effectively reduces the number of fine-tuned parameters while enhancing the flexibility of rank-level representations. Moreover, we present a rigorous theoretical analysis to demonstrate that the proposed subspace-induced dynamic LoRA achieves lower approximation errors compared to static-weighted approaches. Extensive experiments on benchmarks for commonsense reasoning and natural language understanding validate the superiority of DMLoRA over conventional LoRA and its variants, showcasing its efficacy in both parameter efficiency and expressive capacity. The code of this paper is available at https://github.com/MobiusDai/DMLoRA
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions
Authors: Jing Jiang, Chunxu Zhang, Honglei Zhang, Zhiwei Li, Yidong Li, Bo Yang
Venue: WWW
Year: 2025

Personalization stands as the cornerstone of recommender systems (RecSys), striving to sift out redundant information and offer tailor-made services for users. However, the conventional cloud-based RecSys necessitate centralized data collection, posing significant risks of user privacy breaches. In response to this challenge, federated recommender systems (FedRecSys) have emerged, garnering considerable attention. FedRecSys enable users to retain personal data locally and solely share model parameters with low privacy sensitivity for global model training, significantly bolstering the system's privacy protection capabilities. Within the distributed learning framework, the pronounced non-iid nature of user behavior data introduces fresh hurdles to federated optimization. Meanwhile, the ability of federated learning to concurrently learn multiple models presents an opportunity for personalized user modeling. Consequently, the development of personalized FedRecSys (PFedRecSys) is crucial and holds substantial significance. This tutorial seeks to provide an introduction to PFedRecSys, encompassing (1) an overview of existing studies on PFedRecSys, (2) a comprehensive taxonomy of PFedRecSys spanning four pivotal research directions-client-side adaptation, server-side aggregation, communication efficiency, privacy and protection, and (3) exploration of open challenges and promising future directions in PFedRecSys. This tutorial aims to establish a robust foundation and spark new perspectives for subsequent exploration and practical implementations in the evolving realm of RecSys.
Retrieval and Structuring Augmented Generation with LLMs for Web Applications
Authors: Yizhu Jiao, Siru Ouyang, Ming Zhong, Yunyi Zhang, Linyi Ding, Sizhe Zhou, Jiawei Han
Venue: WWW
Year: 2025

Although Large Language Models (LLMs) have revolutionized natural language processing, they face significant challenges in web applications, including hallucinations, outdated knowledge, and limited specialization in niche domains. To address these issues, this tutorial explores how integrating retrieval mechanisms and structured knowledge can enhance LLM performance for web use. By leveraging Retrieval-Augmented Generation (RAG), we can ground LLM outputs with relevant external data, mitigating limitations in applications like search engines, chatbots, and recommendation systems. We delve into text structuring techniques-such as taxonomy construction, multi-level text classification, and taxonomy-guided information retrieval-that improve the effectiveness of information retrieval processes. Furthermore, we examine how structure-guided augmented generation through information extraction and knowledge graph construction can reduce hallucinations and enhance factual accuracy. By bridging the gap between unstructured language models and structured knowledge, we aim to unlock new potentials for dynamic web content generation and personalized user experiences. Finally, we highlight future directions for seamlessly integrating retrieval and generation, enhancing personalization, and incorporating multimodal data to expand the capabilities of LLMs in web applications.
ScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction
Authors: Yiqiao Jin, Stefano Petrangeli, Yu Shen, Gang Wu
Venue: WWW
Year: 2025

Graphical User Interface (GUI) agents are autonomous systems that interpret and generate actions, enabling intelligent user assistance and automation. Effective training of these agent presents unique challenges, such as sparsity in supervision signals, scalability for large datasets, and the need for nuanced user understanding. We propose stateful screen schema, an efficient representation of GUI interactions that captures key user actions and intentions over time. Building on this foundation, we introduce ScreenLLM, a set of multimodal large language models (MLLMs) tailored for advanced UI understanding and action prediction. Extensive experiments on both open-source and proprietary models show that ScreenLLM accurately models user behavior and predicts actions. Our work lays the foundation for scalable, robust, and intelligent GUI agents that enhance user interaction in diverse software environments.
IntentionGPT: Boosting E-commerce User Intent Recognition with Self-data Augmentation and Structure-aware Retrieval
Authors: Yilun Jin, Chunqi Wu, Ying Zhang, Chenglong Shi, Zhao Li, Wei Yin, Xuming Pan
Venue: WWW
Year: 2025

In recent years, Vision Language Models (VLMs) have significantly advanced multi-modal reasoning and generation tasks, offering promising solutions for e-commerce applications like multi-modal user intent recognition. However, VLMs trained on open-domain datasets often struggle with understanding fine-grained e-commerce semantics. This limitation stems primarily from two key factors: the scarcity of labeled multi-modal data for training and the lack of domain-specific knowledge to effectively reason. To overcome the above problems, we introduce IntentionGPT, a novel framework for multi-modal user intent recognition in e-commerce with limited labeled data. Specifically, we first propose a self-data augmentation method to generate diverse synthetic samples from minimal seed labeled samples to tackle data scarcity challenge. Meanwhile, a new collaborative filtering mechanism is proposed to filter out noise samples to enhance data quality. Second, a structure-aware retrieval method is proposed for retrieving domain knowledge to enhance the model's multi-modal reasoning ability. Third, the model soups method are utilized to fuse models from different training stages, combining their strengths and mitigating potential biases for robust performance. This work systematically addresses both data scarcity and domain adaptation challenges, and achieves first place in the WWW2025 multi-modal dialogue system intent recognition challenge.
Abstractive Aspect-Based Comparative Summarization
Authors: Hyeon Jin, Chaewon Yoon, Yurim Oh, Hyun-Je Song
Venue: WWW
Year: 2025

Comparative summarization aims to generate a set of summaries that highlight the relevant differences and commonalities between two comparable entities. While these summaries provide users with general comparative information, they may not fully meet users' specific information needs, particularly when users seek detailed information about specific aspects of the entities. In this paper, we introduce the task of abstractive aspect-based comparative summarization, which identifies the aspects of entities from a set of two reviews and then generates abstractive contrastive and common summaries for each aspect. To support this task, we construct two new datasets and propose a simple large language model-based summarization model that generates both aspects and the corresponding contrastive and common summaries. Experimental results on the newly constructed datasets demonstrate that the proposed summarization model can generate higher-quality aspect-based comparative summaries compared to the baselines.
IP-VQA Dataset: Empowering Precision Agriculture with Autonomous Insect Pest Management through Visual Question Answering
Authors: Kairui Jin, Xing Zi, Karthick Thiyagarajan, Ali Braytee, Mukesh Prasad
Venue: WWW
Year: 2025

Precision agriculture is essential for social good, global economy and food security, yet insect pests threaten productivity through crop damage, pathogen spread, and rising pest control costs. The overuse of pesticides leads to environmental issues and pesticide resistance. Advanced technologies like Visual Question Answering (VQA) provide solutions by integrating image processing with natural language understanding, facilitating efficient pest detection and crop health monitoring. While datasets like IP102 have enhanced pest recognition, they lack necessary question-answer pairs for VQA tasks in agriculture. To address this gap, we introduce the Insect Pest Visual Question Answering (IP-VQA) dataset, designed specifically for precision agricultural applications. This dataset includes a diverse collection of high-quality images annotated with detailed question-answer pairs related to crop health, pest identification, and agricultural practices. Our thorough data collection ensures reliability and relevance. We also utilize advanced multimodal large language models to set a benchmark for the dataset. The primary contribution of the IP-VQA dataset lies in its comprehensive coverage and VQA integration within agricultural contexts. By providing rich visual and textual information, it connects VQA techniques to practical agricultural needs, supporting ongoing research and paving the way for future studies in precision agriculture.
Revealing, Analyzing, and Mitigating the Adverse Impact of Shared Accounts on Personalized Recommendations for Single-User Accounts
Authors: Xingbo Jing, Tao Lian, Li Wang
Venue: WWW
Year: 2025

Shared accounts are not uncommon in some applications, which can breed spurious associations between items that are actually consumed by distinct members. This may adversely affect the recommendations for other single-user accounts, which remains unexplored. To reveal this problem, we present a fair comparison of the recommendation performance for the same subset of single-user accounts under two settings: A controlled setting with only single-user accounts, and an experimental setting with both single-user accounts and shared accounts. The results show that the presence of shared accounts can lead to a performance decrease of up to 10%--40% for other single-user accounts. To gain deeper insights, we define two quantitative metrics to analyze the changes in the proximity of demoted relevant items and promoted irrelevant items to the user's consumed items in the latent space. The results show that the performance degradation can be attributed to the distortion of proximity relations between item embeddings. To mitigate the performance degradation, we leverage the multimodal information of items to construct a reliable item-item semantic graph which is fused with the bipartite behavior graph to counterbalance spurious second-order item-item associations arising from shared accounts. The results show that our mitigation method can effectively alleviate the performance degradation for other single-user accounts caused by shared accounts, recovering or even surpassing the recommendation performance in the controlled setting without shared accounts.
A Concept for Integrating an LLM-Based Natural Language Interface for Visualizations Grammars
Authors: Adrian Jobst, Daniel Atzberger, Mariia Tytarenko, Willy Scheibel, Jürgen Döllner, Tobias Schreck
Venue: WWW
Year: 2025

In this paper, we propose a natural language interface visualization framework leveraging visualization grammar to balance the flexibility and stability of generated visualizations. Our system employs a JSON schema for visualization specification and an instruction prompt with semantically distinct sections for task context, visualizations, datasets, and control mechanisms. This design enables robust state management, live prompt adjustments, ensures clarity, consistency, and reusability in visualization generation.
The AI Ally Project: Designing a Human-Centred AI Tool for Women's Safety in Online Spaces
Authors: Dahlia Jovic, Ren Galwey, Lucy A. Sparrow, Mahli-Ann R. Butt, Yige Song, Sable Wang-Wills, Eduardo A. Oliveira
Venue: WWW
Year: 2025

Online gender-based harassment is a widespread and persistent issue that disproportionately affects girls, young women, and gender diverse individuals. Systemic barriers, such as ineffective reporting mechanisms and fear of retaliation, often prevent victims and bystanders from taking action to address harm. These barriers emphasise the need for innovative, human-centred approaches to online safety. The AI Ally project is developing a trauma-informed, co-designed artificial intelligence (AI) tool that supports victims and witnesses of online harassment. The tool provides victims and witnesses with practical ways to document, reflect on, and report harassment, with the goal of empowering users and promoting safer digital environments. This paper presents preliminary findings from the AI Ally project, drawing on over 230 survey responses from girls, young women, and gender diverse individuals living in Australia. We outline how survey insights, guided by a Feminist Participatory Action Research (FPAR) framework, are shaping the tool's design through a collaborative approach with participants. Additionally, we discuss how AI Ally's core functionalities and design considerations aim to bridge critical gaps in existing moderation systems and harassment reporting tools. This research contributes to ongoing discussions on ethical AI and technologies that are designed to be inclusive and responsive to diverse user needs.
ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling
Authors: Robert J. Joyce, Derek Everett, Maya Fuchs, Edward Raff, James Holt
Venue: WWW
Year: 2025

Determining the family to which a malicious file belongs is an essential component of cyberattack investigation, attribution, and remediation. Performing this task manually is time consuming and requires expert knowledge. Automated tools using that label malware using antivirus detections lack accuracy and/or scalability, making them insufficient for real-world applications. Three pervasive shortcomings in these tools are responsible: (1) incorrect parsing of antivirus detections, (2) errors during family alias resolution, and (3) an inappropriate antivirus aggregation strategy. To address each of these, we created our own malware family labeling tool called ClarAVy. ClarAVy utilizes a Variational Bayesian approach to aggregate detections from a collection of antivirus products into accurate family labels. Our tool scales to enormous malware datasets, and we evaluated it by labeling ?40 million malicious files. ClarAVy has 8 and 12 percentage points higher accuracy than the prior leading tool in labeling the MOTIF and MalPedia datasets, respectively.
TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation
Authors: Chenlu Ju, Jiaxin Liu, Shobhit Sinha, Hao Xue, Flora Salim
Venue: WWW
Year: 2025

This work leverages Large Language Models (LLMs) to simulate human mobility, addressing challenges like high costs and privacy concerns in traditional models. Our hierarchical framework integrates persona generation, activity selection, and destination prediction, using real-world demographic and psychological data to create realistic movement patterns. Both physical models and language models are employed to explore and demonstrate different methodologies for human mobility simulation. By structuring data with summarization and weighted density metrics, the system ensures scalable memory management while retaining actionable insights. Preliminary results indicate that LLM-driven simulations align with observed real-world patterns, offering scalable, interpretable insights for social problems such as urban planning, traffic management, and public health. The framework's ability to dynamically generate personas and activities enables it to provide adaptable and realistic daily routines. This study demonstrates the transformative potential of LLMs in advancing mobility modeling for societal and urban applications. The source code and interactive demo for our framework are available at https://github.com/cju0/TrajLLM.
Co-Trained Retriever-Generator Framework for Question Generation in Earnings Calls
Authors: Yining Juan, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2025

In diverse professional environments, ranging from academic conferences to corporate earnings calls, the ability to anticipate audience questions stands paramount. Traditional methods, which rely on manual assessment of an audience's background, interests, and subject knowledge, often fall short-particularly when facing large or heterogeneous groups, leading to imprecision and inefficiency. While NLP has made strides in text-based question generation, its primary focus remains on academic settings, leaving the intricate challenges of professional domains, especially earnings call conferences, underserved. Addressing this gap, our paper pioneers the multi-question generation (MQG) task specifically designed for earnings call contexts. Our methodology involves an exhaustive collection of earnings call transcripts and a novel annotation technique to classify potential questions. Furthermore, we introduce a retriever-enhanced strategy to extract relevant information. With a core aim of generating a spectrum of potential questions that analysts might pose, we derive these directly from earnings call content. Empirical evaluations underscore our approach's edge, revealing notable excellence in the accuracy, consistency, and perplexity of the questions generated.
Balancing Graph Embedding Smoothness in Self-supervised Learning via Information-Theoretic Decomposition
Authors: Heesoo Jung, Hogun Park
Venue: WWW
Year: 2025

Self-supervised learning (SSL) in graphs has garnered significant attention, particularly in employing Graph Neural Networks (GNNs) with pretext tasks initially designed for other domains, such as contrastive learning and feature reconstruction. However, it remains uncertain whether these methods effectively reflect essential graph properties, precisely representation similarity with its neighbors. We observe that existing methods position opposite ends of a spectrum driven by the graph embedding smoothness, with each end corresponding to outperformance on specific downstream tasks. Decomposing the SSL objective into three terms via an information-theoretic framework with a neighbor representation variable reveals that this polarization stems from an imbalance among the terms, which existing methods may not effectively maintain. Further insights suggest that balancing between the extremes can lead to improved performance across a wider range of downstream tasks. A framework, BSG (Balancing Smoothness in Graph SSL), introduces novel loss functions designed to supplement the representation quality in graph-based SSL by balancing the derived three terms: neighbor loss, minimal loss, and divergence loss. We present a rigorous theoretical analysis of the effects of these loss functions, highlighting their significance from both the SSL and graph smoothness perspectives. Extensive experiments on multiple real-world datasets across node classification and link prediction consistently demonstrate that BSG achieves state-of-the-art performance, outperforming existing methods. Our implementation code is available at https://github.com/steve30572/BSG.