OpenRaiser/Intern-Atlas huggingface dataset:
FuXi-α: Scaling Recommendation Model with Feature Interaction Enhanced Transformer
Authors: Yufei Ye, Wei Guo, Jin Yao Chin, Hao Wang, Hong Zhu, Xi Lin, Yuyang Ye, Yong Liu, Ruiming Tang, Defu Lian, Enhong Chen
Venue: WWW
Year: 2025

Inspired by scaling laws and large language models, research on large-scale recommendation models has gained significant attention. Recent advancements have shown that expanding sequential recommendation models to large-scale recommendation models can be an effective strategy. Current state-of-the-art sequential recommendation models primarily use self-attention mechanisms for explicit feature interactions among items, while implicit interactions are managed through Feed-Forward Networks (FFNs). However, these models often inadequately integrate temporal and positional information, either by adding them to attention weights or by blending them with latent representations, which limits their expressive power. A recent model, HSTU, further reduces the focus on implicit feature interactions, constraining its performance. We propose a new model called FuXi-α to address these issues. This model introduces an Adaptive Multi-channel Self-attention mechanism that distinctly models temporal, positional, and semantic features, along with a Multi-stage FFN to enhance implicit feature interactions. Our offline experiments demonstrate that our model outperforms existing models, with its performance continuously improving as the model size increases. Additionally, we conducted an online A/B test within the Huawei Music app, which showed a 4.76% increase in the average number of songs played per user and a 5.10% increase in the average listening duration per user. Our code has been released at https://github.com/USTC-StarTeam/FuXi-alpha.
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection
Authors: Junwei Yin, Min Gao, Kai Shu, Wentao Li, Yinqiu Huang, Zongwei Wang
Venue: WWW
Year: 2025

The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches have emerged, most rely on content analysis with limited semantic depth, leading to suboptimal comprehension of news content. To address this limitation, capturing broader-range semantics is essential yet challenging, as it introduces two primary types of noise: fully connecting sentences in news graphs often adds unnecessary structural noise, while highly similar but authenticity-irrelevant sentences introduce feature noise, complicating the detection process. To tackle these issues, we propose BREAK, a broad-range semantics model for fake news detection that leverages a fully connected graph to capture comprehensive semantics while employing dual denoising modules to minimize both structural and feature noise. The semantic structure denoising module balances the graph's connectivity by iteratively refining it between two bounds: a sequence-based structure as a lower bound and a fully connected graph as the upper bound. This refinement uncovers label-relevant semantic interrelations structures. Meanwhile, the semantic feature denoising module reduces noise from similar semantics by diversifying representations, aligning distinct outputs from the denoised graph and sequence encoders using KL-divergence to achieve feature diversification in high-dimensional space. The two modules are jointly optimized in a bi-level framework, enhancing the integration of denoised semantics into a comprehensive representation for detection. Extensive experiments across four datasets prove that BREAK significantly outperforms existing fake news detection methods.
Graph Condensation: Foundations, Methods and Prospects
Authors: Hongzhi Yin, Xinyi Gao, Junliang Yu, Ruihong Qiu, Tong Chen, Quoc Viet Hung Nguyen, Zi Huang
Venue: WWW
Year: 2025

Graph data is extensively utilized across various domains, owing to its capacity to represent complex structural relationships among diverse real-world entities. However, the rapid expansion of graph data introduces significant challenges in terms of storage, transmission, and the training of graph neural networks (GNNs) for effective graph data analysis. In light of these challenges, graph condensation (GC) has emerged as a data-centric solution, synthesizing a compact yet representative graph to replace the original large graph in GNN training. These GNNs trained on condensed graphs can achieve performance comparable to models trained on full-scale data, attracting substantial attention and stimulating extensive research. In response to this trend, this tutorial provides a comprehensive and up-to-date overview of GC research. It systematically categorizes existing studies into five categories aligned with critical GC evaluation criteria: effectiveness, generalization, efficiency, fairness, and robustness. Additionally, we will provide an in-depth analysis of two fundamental components of GC: optimization strategies and condensed graph generation, elucidating their key characteristics and underlying technologies. Finally, this tutorial will explore GC applications across various fields and outline potential directions for future research in this rapidly evolving and impactful domain. To help the audience gain a systematic understanding of the topics and technologies covered in this tutorial, we present further details in our recent survey paper https://arxiv.org/abs/2401.11720 and GC toolkit https://arxiv.org/abs/2405.14246
KOE: A Key Distribution-Oriented Performance Estimation Framework for Diverse Index Structures
Authors: Yiheng You, Xin Wang, Jiake Ge, Hui Wang, Menglu Ma, Yongqi Yin
Venue: WWW
Year: 2025

Index structures are a fundamental component of web search and modern database systems, playing a crucial role in enabling efficient data retrieval and management. However, selecting the most appropriate index structure for specific scenarios and workloads is a highly challenging task. This is especially true for learned indexes, where the cumulative distribution characteristics of stored keys not only affects performance but also impacts the robustness of the index. In this paper, we propose a key distribution-aware framework called KOE, to estimate the performance and robustness of various index structures. The framework predicts two critical metrics-performance and memory consumption-across various data distributions and workloads, integrating both temporal and statistical characteristics through a novel learning-based approach, denoted as TStatE. Experimental results demonstrate that KOE provides accurate, data-driven performance and robustness estimation, facilitates systematic index selection and optimization, and can effectively generalize across different index types and workloads, making it a robust tool for exploring trade-offs in index deployment.
Joint Optimal Transport and Embedding for Network Alignment
Authors: Qi Yu, Zhichen Zeng, Yuchen Yan, Lei Ying, R. Srikant, Hanghang Tong
Venue: WWW
Year: 2025

Network alignment, which aims to find node correspondence across different networks, is the cornerstone of various downstream multi-network and Web mining tasks. Most of the embedding-based methods indirectly model cross-network node relationships by contrasting positive and negative node pairs sampled from hand-crafted strategies, which are vulnerable to graph noises and lead to potential misalignment of nodes. Another line of work based on the optimal transport (OT) theory directly models cross-network node relationships and generates noise-reduced alignments. However, OT methods heavily rely on fixed, pre-defined cost functions that prohibit end-to-end training and are hard to generalize. In this paper, we aim to unify the embedding and OT-based methods in a mutually beneficial manner and propose a joint optimal transport and embedding framework for network alignment named JOENA. For one thing (OT for embedding), through a simple yet effective transformation, the noise-reduced OT mapping serves as an adaptive sampling strategy directly modeling all cross-network node pairs for robust embedding learning. For another (embedding for OT), on top of the learned embeddings, the OT cost can be gradually trained in an end-to-end fashion, which further enhances the alignment quality. With a unified objective, the mutual benefits of both methods can be achieved by an alternating optimization schema with guaranteed convergence. Extensive experiments on real-world networks validate the effectiveness and scalability of JOENA, achieving up to 16% improvement in MRR and 20 times speedup compared with the state-of-the-art alignment methods.
SimEdge: A Scalable Transitivity-Aware Graph-Theoretic Similarity Model for Capturing Edge-to-Edge Relationships
Authors: Weiren Yu
Venue: WWW
Year: 2025

Measuring similarity based on network topology is a crucial task in the realm of web search. While many well-established similarity measures (e.g. SimRank) focus on assessing node-to-node similarity, capturing edge-to-edge relationships is equally important in many applications (e.g. link spam detection). However, existing node-to-node similarity measures from the SimRank family may violate the triangular inequality. When applied directly to assessing edge-to-edge similarity, such measures may fail to capture transitive relationships and misrepresent dissimilarity between nodes. In this paper, we propose a novel similarity measure, SimEdge, which can capture transitive relationships for assessing edge-to-edge similarity. The intuition of SimEdge revolves around a mutual reinforcement co-recursion: "two edges are assessed as similar if they are linked to similar nodes, and two nodes are assessed as similar if they are linked to similar edges." We show that SimEdge guarantees the transitivity of similarity, and enhances the accuracy of the node-to-node SimRank similarity without misrepresenting dissimilarity between nodes. For large-scale graphs, we also propose efficient techniques to compute SimEdge similarities in linear memory with guaranteed accuracy. Our empirical evaluation on various datasets validates that SimEdge is highly effective in capturing transitive edge-to-edge relationships, while offering a more reliable assessment of node-to-node similarity. Moreover, SimEdge shows superior scalability in assessing edge-to-edge similarities on large-scale graphs with billions of edges.
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Authors: Lulu Yu, Keping Bi, Jiafeng Guo, Shihao Liu, Dawei Yin, Xueqi Cheng
Venue: WWW
Year: 2025

Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and observed (typically modeled by position). However, in real-world scenarios, users often click only one or two results after examining multiple relevant options, due to limited patience or because their information needs have already been satisfied. Motivated by this, we propose a query-level click propensity model to capture the probability that users will click on different result lists, allowing for non-zero probabilities that users may not click on an observed relevant result. We hypothesize that this propensity increases when more potentially relevant results are present, and refer to this user behavior as relevance saturation bias. Our method introduces a Dual Inverse Propensity Weighting (DualIPW) mechanism-combining query-level and position-level IPW-to address both relevance saturation and position bias. Through theoretical derivation, we prove that DualIPW can learn an unbiased ranking model. Experiments on the real-world Baidu-ULTR dataset demonstrate that our approach significantly outperforms state-of-the-art ULTR baselines. The code can be found at https://github.com/Trustworthy-Information-Access/DualIPW.
BiasNavi: LLM-Empowered Data Bias Management
Authors: Junliang Yu, Jay Thai Duong Huynh, Shaoyang Fan, Gianluca Demartini, Tong Chen, Hongzhi Yin, Shazia Sadiq
Venue: WWW
Year: 2025

Bias in datasets undermines the fairness, transparency, and reliability of AI systems, presenting critical challenges across applications. Existing tools for managing data bias often remain inaccessible to non-experts or struggle with the complexities of domain-specific datasets. In this work, we introduce BiasNavi, an large language models (LLM)-empowered toolkit for data bias management. BiasNavi features an autonomous agent that seamlessly integrates with modules for bias identification, measurement, surfacing, and adaptation, reasoning over data and interactions to adaptively guide users through the bias management pipeline. With intuitive and personalized interfaces, BiasNavi empowers users to customize their workflows and address data bias effectively. A case study with the COMPAS dataset demonstrates how BiasNavi, by leveraging the advanced reasoning capabilities of LLMs, democratizes responsible AI practices, making bias management both accessible and effective for users with varying levels of expertise. BiasNavi is available at: https://github.com/CIRES-Hub/BiasNavi.
HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction
Authors: Song Kyung Yu, Da Eun Lee, Yunyong Ko, Sang-Wook Kim
Venue: WWW
Year: 2025

Hyperedge prediction is a fundamental task to predict future high-order relations based on the observed network structure. Existing hyperedge prediction methods, however, suffer from the data sparsity problem. To alleviate this problem, negative sampling methods can be used, which leverage non-existing hyperedges as contrastive information for model training. However, the following important challenges have been rarely studied: (C1) lack of guidance for generating negatives and (C2) possibility of producing false negatives. To address them, we propose a novel hyperedge prediction method, HyGEN, that employs (1) a negative hyperedge generator that employs positive hyperedges as a guidance to generate more realistic ones and (2) a regularization term that prevents the generated hyperedges from being false negatives. Extensive experiments on six real-world hypergraphs reveal that HyGEN consistently outperforms four state-of-the-art hyperedge prediction methods.
LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework
Authors: Zongyou Yu, Qiang Qu, Qian Zhang, Nan Zhang, Xiaoming Chen
Venue: WWW
Year: 2025

Recent advancements in event-based recognition have demonstrated significant promise, yet most existing approaches rely on extensive training, limiting their adaptability for efficient processing of event-driven visual content. Meanwhile, large language models (LLMs) have exhibited remarkable zero-shot capabilities across diverse domains, but their application to event-based visual recognition remains largely unexplored. To bridge this gap, we propose LLM-EvGen, an event representation generator that produces LLM-compatible event representations LLM-EvRep, thereby enhancing the performance of LLMs on event recognition tasks. The generator is trained using a self-supervised framework, aligning the generated representations with semantic consistency and structural fidelity. Comprehensive experiments were conducted on three datasets: N-ImageNet, N-Caltech101, and N-MNIST. The results demonstrate that our method, LLM-EvRep, outperforms the event-to-video method, E2VID, by 15.93%, 0.82%, and 50.21%, respectively, in recognition tasks when evaluated using GPT-4o.
Intelligent Agents with Adaptive Knowledge Fusion for Personalized Recommendation
Authors: Yuanqing Yu, Zhefan Wang, Chumeng Jiang, Xinyi Li, Jiayin Wang, Min Zhang
Venue: WWW
Year: 2025

With powerful semantic understanding, planning, and decision-making capabilities, LLM-based agents have been applied to solve recommendation tasks. However, challenges remain in effectively evolving user preferences within agent modules and in leveraging external knowledge to improve recommendation accuracy. In this paper, we propose an innovative agent-based framework for personalized recommendation systems that integrates adaptive knowledge fusion to address these challenges. Our framework consists of two primary components: an intelligent agent and the fusion of external knowledge. The intelligent agent includes a memory module that simulates human-like retention of user-item interactions and a reasoning module that applies Chain-of-Thought (CoT) techniques to mimic human thought processes. Furthermore, we introduce two main strategies for knowledge fusion: (1) a preranking approach using external knowledge to reorder candidates and reduce bias, and (2) an ensemble method that combines multiple ranking signals for more accurate recommendations. Evaluations on three public datasets and the AgentSociety Challenge demonstrate the framework's effectiveness in enhancing recommendation quality and adaptability. The code is open-sourced.
Enhancing Zero-Shot Knowledge Graph Relation Prediction through Large Language Models and Contrastive Learning
Authors: Hang Yu, Haoyi Yu
Venue: WWW
Year: 2025

Knowledge graph completion (KGC) aims to predict missing facts in a knowledge graph (KG), with zero-shot relation prediction remaining a significant challenge due to limited descriptive information. In this paper, we propose ROCKGC, a framework that leverages large language models (LLMs) to generate enriched relation descriptions and integrates contrastive learning with a conditional variational autoencoder (C-VAE) to enhance relational separation in the embedding space. By combining LLM-generated semantic representations with C-VAE and contrastive loss, ROCKGC effectively captures nuanced relational differences and improves zero-shot learning performance. Extensive experiments on benchmark datasets show that our method outperforms state-of-the-art approaches, achieving superior accuracy and robustness in predicting unseen relations.
A Theory-Driven Approach to Inner Product Matrix Estimation for Incomplete Data: An Eigenvalue Perspective
Authors: Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li
Venue: WWW
Year: 2025

Addressing the critical challenge of data incompleteness in inner product matrix estimation, we introduce a novel eigenvalue correction method designed to precisely reconstruct true inner product matrices from incomplete data. Utilizing random matrix theory, our method adjusts the eigenvalue distribution of the estimated inner product matrix to align with the ground truth. This approach significantly reduces estimation errors for both inner product matrices and the associated Euclidean distance matrices, thereby enhancing the effectiveness of similarity searches on incomplete data. Our method surpasses traditional data imputation and similarity calibration techniques in both maximum inner product search and nearest neighbor search tasks, demonstrating marked advancements in managing incomplete data.
A Contextual-Aware Position Encoding for Sequential Recommendation
Authors: Jun Yuan, Guohao Cai, Zhenhua Dong
Venue: WWW
Year: 2025

Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. A critical component of modern SR models is the attention mechanism, which synthesizes users' historical activities. This mechanism is typically order-invariant and generally relies on position encoding (PE). Conventional SR models simply assign a learnable vector to each position, resulting in only modest gains compared to traditional recommendation models. Moreover, limited research has been conducted on position encoding tailored for sequential recommendation, leaving a significant gap in addressing its unique requirements. To bridge this gap, we propose a novel Contextual-Aware Position Encoding method for sequential recommendation, abbreviated as CAPE. To the best of our knowledge, CAPE is the first PE method specifically designed for sequential recommendation. Comprehensive experiments conducted on benchmark SR datasets demonstrate that CAPE consistently enhances multiple mainstream backbone models and achieves state-of-the-art performance, across small and large scale model size. Furthermore, we deployed CAPE in an industrial setting on a real-world commercial platform, clearly showcasing the effectiveness of our approach. Our source code is available at https://github.com/yjdy/CAPE.
USPTO-LLM: A Large Language Model-Assisted Information-enriched Chemical Reaction Dataset
Authors: Shen Yuan, Shukai Gong, Hongteng Xu
Venue: WWW
Year: 2025

Over the past few years, the machine learning community has given increasing attention to chemical reaction prediction and retrosynthesis. Despite impressive achievements, the existing datasets in this field have gradually become the bottleneck of current research --- the limitation of dataset size and the lack of reaction condition information hinder the practicability of the current methods. In this study, we construct an information-enriched chemical reaction dataset called USPTO-LLM, with the help of large language models (LLMs). This dataset comprises over 247K chemical reactions extracted from the patent documents of USPTO (United States Patent and Trademark Office), encompassing abundant information on reaction conditions. We employ large language models to expedite the data collection procedures automatically with a reliable quality control process. Experiments show that USPTO-LLM helps pre-train the existing retrosynthesis methods and the condition information in the dataset helps improve the model performance. The dataset is open-sourced at https://zenodo.org/records/14396156 and the annotation code is open-sourced at https://github.com/GONGSHUKAI/USPTO_LLM.
KG-UQ: Knowledge Graph-Based Uncertainty Quantification for Long Text in Large Language Models
Authors: Yingqing Yuan, Linwei Tao, Haohui Lu, Matloob Khushi, Imran Razzak, Mark Dras, Jian Yang, Usman Naseem
Venue: WWW
Year: 2025

With the commercialization of large language models (LLMs) and their integration into daily life, addressing their susceptibility to hallucinations-unfactual information in generated outputs-has become an urgent priority. Existing uncertainty quantification (UQ) methods often rely on access to LLMs' internal states, which is unavailable for closed-source models like GPTs, or are primarily designed for short text. Current research on long text typically evaluates sentences individually, overlooking smaller semantic units that better capture the text's complexity. Recognizing the potential of knowledge graphs (KGs) to extract structured relationships from unstructured text, we propose KG-UQ, a UQ method leveraging KGs to address the semantic intricacies of long text. Our approach involves constructing KGs from long-text outputs and utilizing their embeddings to estimate uncertainties. Through our analysis, we demonstrate that knowledge graphs are an effective tool for decomposing long text into fundamental statements. However, we also highlight the increased uncertainty introduced during KG construction, stemming from inherent challenges in accurately capturing all semantic information.