OpenRaiser/Intern-Atlas huggingface dataset:
Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness
Authors: Tahsin Alamgir Kheya, Mohamed Reda Bouadjenek, Sunil Aryal
Venue: WWW
Year: 2025

Recommendation systems are now an integral part of our daily lives. We rely on them for tasks such as discovering new movies, finding friends on social media, and connecting job seekers with relevant opportunities. Given their vital role, we must ensure these recommendations are free from societal stereotypes. Therefore, evaluating and addressing such biases in recommendation systems is crucial. Previous work evaluating the fairness of recommended items fails to capture certain nuances as they mainly focus on comparing performance metrics for different sensitive groups. In this paper, we introduce a set of comprehensive metrics for quantifying gender bias in recommendations. Specifically, we show the importance of evaluating fairness on a more granular level, which can be achieved using our metrics to capture gender bias using categories of recommended items like genres for movies. Furthermore, we show that employing a category-aware fairness metric as a regularization term along with the main recommendation loss during training can help effectively minimize bias in the models' output. We experiment on three real-world datasets, using five baseline models alongside two popular fairness-aware models, to show the effectiveness of our metrics in evaluating gender bias. Our metrics help provide an enhanced insight into bias in recommended items compared to previous metrics. Additionally, our results demonstrate how incorporating our regularization term significantly improves the fairness in recommendations for different categories without substantial degradation in overall recommendation performance.
BAT: Benchmark for Auto-bidding Task
Authors: Alexandra Khirianova, Ekaterina Solodneva, Andrey Pudovikov, Sergey Osokin, Egor Samosvat, Yuriy Dorn, Alexander Ledovsky, Yana Zenkova
Venue: WWW
Year: 2025

The optimization of bidding strategies for online advertising slot auctions presents a critical challenge across numerous digital marketplaces. A significant obstacle to the development, evaluation, and refinement of real-time autobidding algorithms is the scarcity of comprehensive datasets and standardized benchmarks. To address this deficiency, we present an auction benchmark encompassing the two most prevalent auction formats. We implement a series of robust baselines on a novel dataset, addressing the most salient Real-Time Bidding (RTB) problem domains: budget pacing uniformity and Cost Per Click (CPC) constraint optimization. This benchmark provides a user-friendly and intuitive framework for researchers and practitioners to develop and refine innovative autobidding algorithms, thereby facilitating advancements in the field of programmatic advertising. The implementation and additional resources can be accessed at the following repository https://github.com/avito-tech/bat-autobidding-benchmark, https://doi.org/10.5281/zenodo.14794182.
GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs
Authors: Shima Khoshraftar, Niaz Abedini, Amir Hajian
Venue: WWW
Year: 2025

The application of large language models (LLMs) to graph data has attracted a lot of attention recently. LLMs allow us to use deep contextual embeddings from pretrained models in text-attributed graphs, where shallow embeddings are often used for the text attributes of nodes. However, it is still challenging to efficiently encode the graph structure and features into a sequential form for use by LLMs. In addition, the performance of an LLM alone, is highly dependent on the structure of the input prompt, which limits their effectiveness as a reliable approach and often requires iterative manual adjustments that could be slow, tedious and difficult to replicate programmatically. In this paper, we propose GraphiT (Graphs in Text), a framework for encoding graphs into a textual format and optimizing LLM prompts for graph prediction tasks. Here we focus on node classification for text-attributed graphs. We encode the graph data for every node and its neighborhood into a concise text to enable LLMs to better utilize the information in the graph. We then further programmatically optimize the LLM prompts using the DSPy framework to automate this step and make it more efficient and reproducible. GraphiT outperforms our LLM-based baselines on three datasets and we show how the optimization step in GraphiT leads to measurably better results without manual prompt tweaking. We also demonstrated that our graph encoding approach is competitive to other graph encoding methods while being less expensive because it uses significantly less tokens for the same task.
Navigating the Gray Interventions: The Impact of Soft Moderation Techniques Across Social Media Platforms
Authors: Namit Khurana, Mohammad Majid Akhtar, Rahat Masood, Salil S. Kanhere, Benjamin P. Turnbull
Venue: WWW
Year: 2025

The proliferation of misinformation on social media platforms (SMPs) poses significant threats to public discourse, public health, and democracy, which requires effective moderation strategies. This work investigates the usability of soft moderation techniques, such as warning labels and contextual links, designed to mitigate the spread of misinformation while maintaining user autonomy. Using a mixed methods approach, it analyzes quantitative engagement metrics and qualitative user perceptions across platforms including Instagram, X (formally known as Twitter), TikTok, and Threads. The results show that contextual links improve credibility and encourage deeper interactions, especially among younger users, while warning labels raise skepticism but may decrease engagement. The user study draws attention to platform-specific dynamics, demographic differences, and the vital role user education and transparency play in enhancing the effectiveness of moderation. This work fills in gaps in the literature by providing practical insights to help SMPs and policymakers develop knowledgeable and reliable digital environments.
Table Retrieval using LLMs and Semantic Table Similarity
Authors: Udayan Khurana, Sahil Suneja, Horst Samulowitz
Venue: WWW
Year: 2025

Searching for relevant tables in response to a textual phrase or a question is an important task for large tabular data repositories, such as relational databases, CSV files in data lakes, etc. It is somewhat different from the problem of web document search because the subjects of search are tables rather than documents, while the query remains textual. In this paper, we explore a novel technique for table search on large repositories using natural language queries. It is based on a generative methodology that aims to maximize the semantic connection between the query and the resulting tables. Unlike traditional keyword search approaches, our technique can find the needed tables more effectively through deeper semantic concept discovery rather than simply searching for exact keyword matches. Additionally, our technique supports natural language queries rather than plain keyword queries. In this paper, we describe the core ideas, implementation, and effectiveness of our method using two different benchmarks with diverse queries.
Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations
Authors: Hai-Dang Kieu, Minh-Duc Nguyen, Thanh-Son Nguyen, Dung D. Le
Venue: WWW
Year: 2025

Recent advancements in Large Language Models (LLMs) have shown significant potential in enhancing recommender systems. However, addressing the cold-start recommendation problem, where users lack historical data, remains a considerable challenge. In this paper, we introduce KALM4Rec (Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations), a novel framework specifically designed to tackle this problem by requiring only a few input keywords from users in a practical scenario of cold-start user recommendations. KALM4Rec operates in two main stages: candidates retrieval and LLM-based candidates re-ranking. In the first stage, keyword-driven retrieval models are used to identify potential candidates, addressing LLMs' limitations in processing extensive tokens and reducing the risk of generating misleading information. In the second stage, we employ LLMs with various prompting strategies, including zero-shot and few-shot techniques, to re-rank these candidates by integrating multiple examples directly into the LLM prompts. Our extensive evaluation, using Yelp restaurant data from three English-speaking cities and TripAdvisor hotel data, demonstrates that KALM4Rec excels in improving recommendation quality across the two domains and also highlights its potential for widespread applications.
Enhancing News Recommendation with Hierarchical LLM Prompting
Authors: Hai-Dang Kieu, Delvin Ce Zhang, Minh Duc Nguyen, Min Xu, Qiang Wu, Dung D. Le
Venue: WWW
Year: 2025

Personalized news recommendation systems often struggle to effectively capture the complexity of user preferences, as they rely heavily on shallow representations, such as article titles and abstracts. To address this problem, we introduce a novel method, namely PNR-LLM, for Large Language Models for Personalized News Recommendation. Specifically, PNR-LLM harnesses the generation capabilities of LLMs to enrich news titles and abstracts, and consequently improves recommendation quality. PNR-LLM contains a novel module, News Enrichment via LLMs, which generates deeper semantic information and relevant entities from articles, transforming shallow contents into richer representations. We further propose an attention mechanism to aggregate enriched semantic- and entity-level data, forming unified user and news embeddings that reveal a more accurate user-news match. Extensive experiments on MIND datasets show that PNR-LLM outperforms state-of-the-art baselines. Moreover, the proposed data enrichment module is model-agnostic, and we empirically show that applying our proposed module to multiple existing models can further improve their performance, verifying the advantage of our design.
The Agenda-Setting Function of Social Media
Authors: Rachel M. Kim, Ashton Anderson
Venue: WWW
Year: 2025

As people increasingly use social media as a primary news source, it becomes critical to understand how online platforms affect peoples' experience of the news. Through the media effects of agenda-setting and framing, different news sources can vary in their influence on public opinion regarding which issues people consider important and how particular aspects of these issues should be interpreted. However, little is known about how issues and frames shift and segregate across partisan lines as traditional news on social media gets filtered by the selective exposure effects of social media. In this study, we investigate the issues and frames invoked in news article shares across Reddit over 16 years and measure their traditional media and social media partisanship. We measure the change between production (news articles posted on Reddit) and consumption (news articles posted on Reddit, weighted by their score). We find that issues are shared in a co-partisan manner across traditional media and social media lines. Issues are also more polarized in social media than traditional media and more polarized in consumption than production. We find that frames across several issues are also subject to co-partisan sharing behavior. In contrast to the significant polarization of news outlets on Reddit in 2016, issues and frames do not polarize more over time. Finally, looking at case studies of frames within specific issues, we disaggregate the shift from production to consumption by distinguishing between issues where the frames polarize and issues that simply receive less exposure on one side of the political spectrum. Our results give insight into broader phenomena like political polarization by highlighting the dimensions of precisely what polarizes and how polarization occurs. Overall, our study showcases the importance of understanding how social media distorts the perception of the news via its agenda-setting and framing functions.
A Certificate-centric Security Oracle for the Trustworthy Internet
Authors: Hyunsoo Kim, Sangwi Kang, Kyunghan Lee, Taekyoung &quot, Ted&quot, Kwon
Venue: WWW
Year: 2025

The Internet's current security landscape remains fundamentally compromised by inherent protocol vulnerabilities. Existing security solutions have been narrowly focused, addressing isolated protocols or specific attack vectors without developing a comprehensive security framework. Our proposed certificate-centric security oracle represents a paradigm shift, offering universal security services that enable protocols to robustly attest their identities and authentically validate their communications. The security oracle operates in a cross-layer fashion so that any protocol (in any layer) that wishes to use such services can be extended individually with minor efforts (say, adding a few fields to carry a certificate or a signature). We also present a few case studies illustrating how existing protocols can be extended to use the services of the security oracle. Prototype-based experiments are carried out for ICMP and DHCP to demonstrate the practical feasibility of the proposed framework.
ExpressPQDelivery: Toward Efficient and Immediately Deployable Post-Quantum Key Delivery for Web-of-Things
Authors: Jane Kim, Jung-Hun Kang, Hyunwoo Lee, Seung-Hyun Seo
Venue: WWW
Year: 2025

Post-quantum cryptography (PQC) aims to develop quantum-safe algorithms against attacks by a quantum computer. As quantum-safe algorithms require much larger keys in their operation compared to the current RSA/ECC practice, the networking latency significantly increases when executing the protocols with sending such large keys. This problem gets more challenging in the era of Web-of-Things (WoT) with low-memory devices. To tackle the problem, we propose ExpressPQDelivery, which is, to the best of our knowledge, the first immediately deployable protocol to efficiently transport large keys. It leverages the DNS infrastructure, as DNS is close to clients, guaranteeing express key delivery with a short round-trip time (RTT). We split a large PQ key along with a server's signature and feed them into several DNS records. To show the feasibility of ExpressPQDelivery, we instantiate it with TLS 1.3 and demonstrate that it reduces 27% of network latency between a server and a client on average compared to the standard TLS 1.3. We deploy ExpressPQDelivery on a low-capability board with 256 KB RAM, showing a significant high gain (34%).
Beyond Neighbors: Distance-Generalized Graphlets for Enhanced Graph Characterization
Authors: Yeongho Kim, Yuyeong Kim, Geon Lee, Kijung Shin
Venue: WWW
Year: 2025

Graphs are widely used to model complex systems across various domains, including social networks and biological systems. A key task in graph analysis is identifying recurring structural patterns, known as graphlets, which capture connectivity among a fixed-size subset of nodes. While graphlets have been extensively applied in tasks such as measuring graph similarity and identifying communities, conventional graphlets focus only on direct connections between nodes. This limitation overlooks potential insights from more distant relationships within the graph structure. In this paper, we introduce (d,s)-graphlets, a generalization of size-s graphlets that incorporates indirect connections between nodes up to distance d. This new formulation provides a more fine-grained and comprehensive understanding of local graph structures. To efficiently count (d,s)-graphlets in a graph, we present EDGE, an exact counting algorithm that employs optimized combinatorial techniques to significantly reduce computational complexity compared to naive enumeration. Our empirical analysis across diverse real-world datasets demonstrates that (d,s)-graphlets provide superior graph characterization, outperforming conventional graphlets in a graph clustering task. Moreover, our case studies show that (d,s)-graphlets uncover non-trivial insights that would remain undiscovered when using conventional graphlets.
CyberAlly: Leveraging LLMs and Knowledge Graphs to Empower Cyber Defenders
Authors: Minjune Kim, Jeff Wang, Kristen Moore, Diksha Goel, Derui Wang, Ahmad Mohsin, Ahmed Ibrahim, Robin Doss, Seyit Camtepe, Helge Janicke
Venue: WWW
Year: 2025

The increasing frequency and sophistication of cyberattacks demand innovative approaches to strengthen defense capabilities. Training on live infrastructure poses significant risks to organizations, making secure, isolated cyber ranges an essential tool for conducting Red vs. Blue Team training events. These events enable security teams to refine their skills without impacting operational environments. While such training provides a strong foundation, the ever-evolving nature of cyber threats necessitates additional support for effective defense. To address this challenge, we introduce CyberAlly, a knowledge graph-enhanced AI assistant designed to enhance the efficiency and effectiveness of Blue Teams during incident response. Integrated into our cyber range alongside an open-source SIEM platform, CyberAlly monitors alerts, tracks Blue Team actions, and suggests tailored mitigation recommendations based on insights from prior Red vs. Blue Team exercises. This demonstration highlights the feasibility and impact of CyberAlly in augmenting incident response and equipping defenders to tackle evolving threats with greater precision and confidence.
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals
Authors: Tomoyoshi Kimura, Xinlin Li, Osama A. Hanna, Yatong Chen, Yizhuo Chen, Denizhan Kara, Tianshi Wang, Jinyang Li, Xiaomin Ouyang, Shengzhong Liu, Mani Srivastava, Suhas N. Diggavi, Tarek F. Abdelzaher
Venue: WWW
Year: 2025

Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on the scale and quality of multimodal samples. These constraints significantly limit the performance of sensing intelligence in IoT applications, as the heterogeneity and the non-interpretability of time-series signals result in abundant unimodal data but scarce high-quality multimodal pairs. This paper proposes InfoMAE, a cross-modal alignment framework that tackles the challenge of multimodal pair efficiency under the SSL setting by facilitating efficient cross-modal alignment of pretrained unimodal representations. InfoMAE achieves efficient cross-modal alignment with limited data pairs through a novel information theory-inspired formulation that simultaneously addresses distribution-level and instance-level alignment. Extensive experiments on two real-world IoT applications are performed to evaluate InfoMAE's pairing efficiency to bridge pretrained unimodal models into a cohesive joint multimodal model. InfoMAE enhances downstream multimodal tasks by over 60% with significantly improved multimodal pairing efficiency. It also improves unimodal task accuracy by an average of 22%.
FL@FM-TheWebConf'25: International Workshop on Federated Foundation Models for the Web 2025
Authors: Irwin King, Guodong Long, Zenglin Xu, Yifei Zhang, Han Yu
Venue: WWW
Year: 2025

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf'25 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field.
Towards Democratized Machine Learning: A Semantic Web Approach
Authors: Antonis Klironomos
Venue: WWW
Year: 2025

The rapid growth of machine learning (ML) research has produced a vast and expanding collection of algorithms, datasets, and pipelines available on the Web. However, fragmented and dispersed documentation of these resources hampers accessibility, transparency, and effective use, posing challenges for users seeking to understand, adapt, and create ML pipelines. To address these challenges, we leverage Knowledge Graphs (KGs) and ontologies to represent ML pipelines as executable KGs (ExeKGs). This approach fosters an intuitive understanding of pipeline components and their relationships while defined constraints streamline the creation of valid and efficient pipelines. Furthermore, the structure of our KGs enables intelligent exploration and discovery of relevant ML artifacts, including pipelines and datasets. By incorporating KG-based ML techniques, we enhance the discovery and reuse of these artifacts. To consolidate these functionalities and provide users with an intuitive interface, we are developing ExeKGLab, a GUI-based platform for interacting with ExeKGs. This thesis explores the potential of KGs to democratize the ML landscape. We present our ongoing efforts to build a KG for ML, emphasizing its role in simplifying pipeline design, enhancing comprehension, and enabling smart exploration. By creating a structured and interconnected framework, our approach seeks to bridge gaps in accessibility and foster a more collaborative ML ecosystem. We invite discussion and feedback to advance this promising direction for future ML research.
CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
Authors: Yunyong Ko, Seongeun Ryu, Sang-Wook Kim
Venue: WWW
Year: 2025

Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem. Despite the breakthrough in personalized news recommendation, the following challenges have been rarely explored: (C1) Comprehending manifold intents coupled within a news article, (C2) Differentiating varying post-read preferences of news articles, and (C3) Addressing the cold-start user problem. To tackle these challenges together, we propose a novel personalized news recommendation framework (CROWN) that employs (1) category-guided intent disentanglement for (C1), (2) consistency-based news representation for (C2), and (3) GNN-enhanced hybrid user representation for (C3). Furthermore, we incorporate a category prediction into the training process of CROWN as an auxiliary task for enhancing intent disentanglement. Extensive experiments on two real-world datasets reveal that (1) CROWN outperforms twelve state-of-the-art news recommendation methods and (2) the proposed strategies significantly improve the accuracy of CROWN.
Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning
Authors: Youmin Ko, Hyemin Yang, Taeuk Kim, Hyunjoon Kim
Venue: WWW
Year: 2025

Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based methods focus solely on encoding textual information, neglecting the long-tailed nature of knowledge graphs and their various topological structures, e.g., subgraphs, shortest paths, and degrees. We claim that this is a major obstacle to achieving higher accuracy of PLMs for KGC. To this end, we propose a Subgraph-Aware Training framework for KGC (SATKGC) with two ideas: (i) subgraph-aware mini-batching to encourage hard negative sampling and to mitigate an imbalance in the frequency of entity occurrences during training, and (ii) new contrastive learning to focus more on harder in-batch negative triples and harder positive triples in terms of the structural properties of the knowledge graph. To the best of our knowledge, this is the first study to comprehensively incorporate the structural inductive bias of the knowledge graph into fine-tuning PLMs. Extensive experiments on three KGC benchmarks demonstrate the superiority of SATKGC. Our code is available.https://github.com/meaningful96/SATKGC