OpenRaiser/Intern-Atlas huggingface dataset:
MGF-ESE: An Enhanced Semantic Extractor with Multi-Granularity Feature Fusion for Code Summarization
Authors: Xiaolong Xu, Yuxin Cao, Hongsheng Hu, Haolong Xiang, Lianyong Qi, Junqun Xiong, Wanchun Dou
Venue: WWW
Year: 2025

Code summarization aims to generate concise natural language descriptions of source code, helping developers to acquaint with software systems and reduce maintenance costs. Existing code summarization approaches widely employ attention mechanisms to assess the relevance between nodes in the Abstract Syntax Tree (AST), which generates context vectors that reflect the semantics of the source code. However, these approaches solely relying on AST lack the extraction of features at other levels of granularity, such as code tokens and Control Flow Graph (CFG), which suffer from severe semantic gaps when capturing data and control dependencies. To address this issue, we design an enhanced semantic extractor with multi-granularity feature fusion (MGF-ESE) to improve the model capability in comprehending and processing the overall semantics of the code. Specifically, we present a novel AST generation method that, based on controlling the scale of nodes, introduces syntactic description nodes to raise the semantic density of AST feature. Then we perform both local and global encoding of CFG after embedding the statement nodes. Moreover, through a cross-attention mechanism, we fuse code tokens and CFG with AST to enhance the model's capacity to capture both syntactic and structural information from source code. Finally, extensive experiments on two open-source datasets show that MGF-ESE outperforms the state-of-the-arts with higher-quality code summaries on key metrics, including BLEU, METEOR, and ROUGE-L.
AI for Women's Health: Framework and Focus Areas
Authors: Jie Lu, Angela Dawson, Kairui Guo, Deborah Fox, Fahimeh Ramezani, Susan Morton, Kathleen Baird, Zelia Soo, Kezhi Lu, Guodong Long, Debra Anderson
Venue: WWW
Year: 2025

AI for women's health is an open challenge that needs to be addressed by cross-disciplinary efforts. There is an urgent need to establish a new type of collaboration that focuses on integrating and adopting AI methods in healthcare to bridge the gap between the aspiration to apply AI across all aspects of women's health and its practical adoption in everyday clinical practice. This is particularly important for women's health where we know there have been significant lags in finding innovative ways to manage prevalent and burdensome health issues that are experienced proportionately more or only by females across their life courses. This position paper will present our initial development including a comprehensive framework, a high-level methodology and main focus areas of advanced AI for Women's Health. The research aims to support women's health services worldwide by providing new tools and novel applications to improve women's health system efficiency and personalized medicine.
LLGformer: Learnable Long-range Graph Transformer for Traffic Flow Prediction
Authors: Di Jin, Cuiying Huo, Jiayi Shi, Dongxiao He, Jianguo Wei, Philip S. Yu
Venue: WWW
Year: 2025

Traffic prediction plays a pivotal role in intelligent transportation systems. Most existing studies only predict traffic flow for a specific time period based on traffic data from a short period, such as an hour, overlooking the influence of periodicity present in traffic data. Moreover, most of the existing advanced methods rely on manually constructed spatio-temporal graphs for joint modeling, or use pure spatial and pure temporal modules to separately model spatial and temporal features, which limits the learning of complex spatio-temporal patterns in traffic data due to structural inadequacies in the model. To address these issues, we propose a novel approach by constructing a learnable long-range spatio-temporal graph, which can better capture complex patterns in traffic data. We introduce a new model, LLGformer, which improves upon traditional Transformer-style models, facilitating more efficient learning of traffic flow data by integrating long-range historical information. Leveraging attention mechanisms on a spatiotemporal graph enables direct interaction of information across different time slices and locations. Additionally, we propose two optimization strategies to further boost the speed of training and inference. Extensive experiments on four real-world datasets show that the new model significantly outperforms state-of-the-art methods.
Two-stage Auction Design in Online Advertising
Authors: Zhikang Fan, Lan Hu, Ruirui Wang, Zhongrui Ma, Yue Wang, Qi Ye, Weiran Shen
Venue: WWW
Year: 2025

Modern online advertising systems often involve a substantial number of advertisers in each auction, which results in scalability issues. To address this challenge, two-stage auctions have been designed and implemented in practice. These auctions enable efficient allocation of ad slots among numerous candidate advertisers in a short response time. This approach employs a fast yet coarse model in the first stage to select a small subset of advertisers, followed by a slow, more refined model to determine the final winners. However, existing two-stage auction mechanisms primarily focus on optimizing welfare, overlooking other critical objectives of the platform, such as revenue. In this paper, we propose ad-wise selection metrics, named Max-Wel and Max-Rev, which optimize the platform's welfare and revenue, respectively. These metrics are based on each ad's contribution to the corresponding objective function. We also provide theoretical guarantees for the proposed metrics. Our method is applicable to both welfare and revenue optimizations and can be easily implemented using neural networks. Through extensive experiments conducted on both synthetic and industrial data, we demonstrate the advantages of our proposed selection metrics compared to existing baselines.
Towards Efficient Conversational Recommendations: Expected Value of Information Meets Bandit Learning
Authors: Zhuohua Li, Maoli Liu, Xiangxiang Dai, John C. S. Lui
Venue: WWW
Year: 2025

In conversational recommender systems, interactively presenting queries and leveraging user feedback are crucial for efficiently estimating user preferences and improving recommendation quality. Selecting optimal queries in these systems is a significant challenge that has been extensively studied as a sequential decision problem. The expected value of information (EVOI), which computes the expected reward improvement, provides a principled criterion for query selection. However, it is computationally expensive and lacks theoretical performance guarantees. Conversely, conversational bandits offer provable regret upper bounds, but their query selection strategies yield only marginal regret improvements over non-conversational approaches. To address these limitations, we integrate EVOI within the conversational bandit framework by proposing a new conversational mechanism featuring two key techniques: (1) gradient-based EVOI, which replaces the complex Bayesian updates in conventional EVOI with efficient stochastic gradient descent, significantly reducing computational complexity and facilitating theoretical analysis; and (2) smoothed key term contexts, which enhance exploration by adding random perturbations to uncover more specific user preferences. Our approach applies to both Bayesian (Thompson Sampling) and frequentist (UCB) variants of conversational bandits. We introduce two new algorithms, ConTS-EVOI and ConUCB-EVOI, and rigorously prove that they achieve substantially tighter regret bounds, with both algorithms offering a √d improvement in their dependence on the time horizon T, where d is the dimension of the feature space. Extensive evaluations on synthetic and real-world datasets validate the effectiveness of our methods.
Highly-efficient Minimization of Network Connectivity in Large-scale Graphs
Authors: Mingyang Zhou, Gang Liu, Kezhong Lu, Hao Liao, Rui Mao
Venue: WWW
Year: 2025

Network connectivity minimization is a fundamental problem in controlling the spread of viruses in the Internet and facilitating information propagation in online social networks. The problem aims to identify a budget number of key nodes whose removal would minimize the connectivity of a network. However, the existing solutions heavily rely on the number of edges, making it challenging to handle large and densely connected social networks. In this study, we present a fast algorithm that is independent of the number of edges. To achieve this, we first introduce a surrogate matrix that approximates the residual adjacency matrix with arbitrary small predefined error. We then devise an efficient approach for inferring k influential nodes by optimizing the eigenvalues of the surrogate matrix. Remarkably, the algorithm has a small time complexity of O(knr3), with r being a small tunable number. Our algorithm thereby maintains a linear scalability in terms of the number of nodes and is unaffected by the number of edges. Hence, it has the capability to efficiently handle large and dense social networks. At last, we evaluate its performance against state-of-the-art techniques using diverse real-world datasets. The experimental results demonstrate the superiority of our proposed method in terms of both solution quality and computational efficiency.
Efficient and Practical Approximation Algorithms for Advertising in Content Feeds
Authors: Guangyi Zhang, Ilie Sarpe, Aristides Gionis
Venue: WWW
Year: 2025

Content feeds provided by platforms such as X (formerly Twitter) and TikTok are consumed by users on a daily basis. In this paper, we revisit the native advertising problem in content feeds, initiated by Ieong et al. Given a sequence of organic items (e.g., videos or posts) relevant to a user's interests or to an information search, the goal is to place ads within the organic content so as to maximize a reward function (e.g., number of clicks), while accounting for two considerations: (1) an ad can only be inserted after a relevant content item; (2) the users' attention decays after consuming content or ads. These considerations provide a natural model for capturing both the advertisement effectiveness and the user experience. In this paper, we design fast and practical 2-approximation greedy algorithms for the associated optimization problem, improving over the best-known practical algorithm that only achieves an approximation factor of 4. Our algorithms exploit a counter-intuitive observation, namely, while top items are seemingly more important due to the decaying attention of the user, taking good care of the bottom items is key for obtaining improved approximation guarantees. We then provide the first comprehensive empirical evaluation on the problem, showing the strong empirical performance of our~methods.
Large Language Model for E-Commerce Workshop
Authors: Haoyu Han, Xianfeng Tang, Jing Huang, Monica Xiao Cheng, Omar Alonso, Hanqing Lu, Zhen Li, Chen Luo, Dawei Yin, Jiliang Tang
Venue: WWW
Year: 2025

Large Language Models (LLMs) are transforming E-Commerce by powering numerous applications such as product recommendation, search, classification, question answering, and advertising. Their growing adoption in real-world systems highlights their potential, yet challenges remain in ensuring accuracy, efficiency, fairness, and privacy. This workshop aims to bring together researchers and industry practitioners to explore the opportunities and limitations of LLMs in e-commerce. Through discussions on model design, algorithmic advancements, and practical deployment strategies, the workshop seeks to foster collaboration, bridge the gap between academia and industry, and drive innovation in applying LLMs to E-Commerce.
Do We Really Need GCNs in Traffic Forecasting? A Graph-Less Pure-MLP Architecture
Authors: Yudong Zhang, Xu Wang, Xuan Yu, Kuo Yang, Zhengyang Zhou, Yang Wang
Venue: WWW
Year: 2025

Traffic flow forecasting, as a fundamental task of intelligent transportation systems driven by the Web, remains challenging due to the complex spatial and temporal dependencies between different roads. Considering the inherent graph structure of road networks, recent works capture the spatial dependencies by utilizing graph convolutional networks (GCNs) and have devoted great efforts to enhancing GCNs. However, graph convolution, as the core of GCNs, has quadratic time and space complexity due to the internal matrix multiplication operation on the adjacency matrix, which prevents existing works from being deployed on huge road networks. In this paper, we doubt the necessity of graph convolution in capturing spatial dependencies and find that the aggregating operation in graph convolution can be replaced by Multi-Layer Perceptron (MLP) with layer normalization in traffic forecasting. Inspired by the emerging MLP models on other deep learning tasks, we further propose a pure-MLP traffic forecasting model without the dependence on graph convolution. Experiments on several real-world traffic datasets show that our pure-MLP model is more efficient and has competitive performances compared to state-of-the-art works.
Towards Non-Euclidean Foundation Models: Advancing AI Beyond Euclidean Frameworks
Authors: Menglin Yang, Yifei Zhang, Jialin Chen, Melanie Weber, Rex Ying
Venue: WWW
Year: 2025

In the era of foundation models and Large Language Models (LLMs), Euclidean space is the de facto geometric setting of our machine learning architectures [9, 31, 33]. However, recent literature has demonstrated that this choice comes with fundamental limitations [2, 12, 15, 29]. To that end, non-Euclidean learning is quickly gaining traction, particularly in web-related applications where complex relationships and structures are prevalent. Non-Euclidean spaces, such as hyperbolic, spherical, and mixed-curvature spaces, have been shown to provide more efficient and effective representations for data with intrinsic geometric properties, including web-related data like social network topology, query-document relationships, and user-item interactions [4, 8, 14, 19, 20, 24, 25, 27, 34, 38, 39, 44, 46, 49]. Integrating foundation models with non- Euclidean geometries has great potential to enhance their ability to capture and model the underlying structures, leading to better performance in search, recommendations, and content understanding. This workshop focuses on the intersection of Non-Euclidean Foundation Models and GEometric Learning (NEGEL), exploring its potential benefits, including the potential benefits for advancing web-related technologies, challenges, and future directions.
Revisiting Backdoor Attacks on Time Series Classification in the Frequency Domain
Authors: Yuanmin Huang, Mi Zhang, Zhaoxiang Wang, Wenxuan Li, Min Yang
Venue: WWW
Year: 2025

Time series classification (TSC) is a cornerstone of modern web applications, powering tasks such as financial data analysis, network traffic monitoring, and user behavior analysis. In recent years, deep neural networks (DNNs) have greatly enhanced the performance of TSC models in these critical domains. However, DNNs are vulnerable to backdoor attacks, where attackers can covertly implant triggers into models to induce malicious outcomes. Existing backdoor attacks targeting DNN-based TSC models remain elementary. In particular, early methods borrow trigger designs from computer vision, which are ineffective for time series data. More recent approaches utilize generative models for trigger generation, but at the cost of significant computational complexity. In this work, we analyze the limitations of existing attacks and introduce an enhanced method, FreqBack. Drawing inspiration from the fact that DNN models inherently capture frequency domain features in time series data, we identify that improper perturbations in the frequency domain are the root cause of ineffective attacks. To address this, we propose to generate triggers both effectively and efficiently, guided by frequency analysis. FreqBack exhibits substantial performance across five models and eight datasets, achieving an impressive attack success rate of over 90%, while maintaining less than a 3% drop in model accuracy on clean data.
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection
Authors: Xiangyu Dong, Xingyi Zhang, Yanni Sun, Lei Chen, Mingxuan Yuan, Sibo Wang
Venue: WWW
Year: 2025

The smoothing issue in graph learning leads to indistinguishable node representations, posing significant challenges for graph-related tasks. However, our experiments reveal that this problem can uncover underlying properties of node anomaly detection (NAD) that previous research has missed. We introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing Patterns (NSP), which indicate that the representations of anomalous nodes are harder to smooth than those of normal ones. In addition, we explore the theoretical implications of these patterns, demonstrating the potential benefits of ISP and NSP for NAD tasks. Motivated by these findings, we propose SmoothGNN, a novel unsupervised NAD framework. First, we design a learning component to explicitly capture ISP for detecting node anomalies. Second, we design a spectral graph neural network to implicitly learn ISP to enhance detection. Third, we design an effective coefficient based on our findings that NSP can serve as coefficients for node representations, aiding in the identification of anomalous nodes. Furthermore, we devise a novel anomaly measure to calculate loss functions and anomalous scores for nodes, reflecting the properties of NAD using ISP and NSP. Extensive experiments on 9 real datasets show that SmoothGNN outperforms the best rival by an average of 14.66% in AUC and 7.28% in Average Precision, with 75x running time speedup, validating the effectiveness and efficiency of our framework. Our code is available at https://github.com/xydong127/SmoothGNN.
Query Understanding in LLM-based Conversational Information Seeking
Authors: Yifei Yuan, Zahra Abbasiantaeb, Yang Deng, Mohammad Aliannejadi
Venue: WWW
Year: 2025

Query understanding in Conversational Information Seeking (CIS) involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. Large Language Models (LLMs) enhance this process by interpreting nuanced language and adapting dynamically, improving the relevance and precision of search results in real-time. In this tutorial, we explore advanced techniques to enhance query understanding in LLM-based CIS systems. We delve into LLM-driven methods for developing robust evaluation metrics to assess query understanding quality in multi-turn interactions, strategies for building more interactive systems, and applications like proactive query management and query reformulation. We also discuss key challenges in integrating LLMs for query understanding in conversational search systems and outline future research directions. Our goal is to deepen the audience's understanding of LLM -based conversational query understanding and inspire discussions to drive ongoing advancements in this field.
DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
Authors: Jiahao Yu, Yihai Duan, Longfei Xu, Chao Chen, Shuliang Liu, Kaikui Liu, Fan Yang, Xiangxiang Chu, Ning Guo
Venue: WWW
Year: 2025

Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to select pre-defined scenarios, which may hinder the downstream ranking performance. In addition, in the academic community, the multi-scenario ranking works only come from other fields, and there are no works specifically focusing on route data due to lacking a publicly available MSRR dataset. Moreover, all the existing multi-scenario works still fail to address the three specific challenges of MSRR simultaneously, i.e. explosion of scenario number, high entanglement, and high-capacity demand. Different from the prior, to address MSRR, our key idea is to factorize the complicated scenario in route ranking into several disentangled factor scenario patterns. Accordingly, we propose a novel method, Disentangled Scenario Factorization Network (DSFNet), which flexibly composes scenario-dependent parameters based on a high-capacity multi-factor-scenario-branch structure. Then, a novel regularization is proposed to induce the disentanglement of factor scenarios. Furthermore, two extra novel techniques, i.e. scenario-aware batch normalization and scenario-aware feature filtering, are developed to improve the network awareness of scenario representation. Additionally, to facilitate MSRR research in the academic community, we propose MSDR, the first large-scale publicly available annotated industrial Multi-Scenario Driving Route dataset. Comprehensive experimental results demonstrate the superiority of our DSFNet, which has been successfully deployed in AMap to serve the major online traffic.
ReadGraph: Relational Evolution Enhanced Anomaly Detection in Dynamic Heterogeneous Graph
Authors: Xiaolin Han, Xiurui Hu, Chenhao Ma, Xuequn Shang
Venue: WWW
Year: 2025

Abnormal behavior detection is crucial in many fields, such as social networks, financial transactions, and cybersecurity. However, it poses significant challenges due to the intricate structural evolution of heterogeneous graphs. To address this issue, we propose a novel method called Relational Evolution enhanced Anomaly Detection in dynamic heterogeneous Graph (ReadGraph ). ReadGraph focuses on tracing relation-based dynamic structural evolution to comprehensively capture features related to abnormal behaviors (edges) across different types of nodes. We conduct extensive experiments to evaluate ReadGraph against advanced competitors. It demonstrates that ReadGraph is 13.69% more effective than other methods on average.
Navigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models
Authors: Haonan Shi, Tu Ouyang, An Wang
Venue: WWW
Year: 2025

Instruction tuning has proven effective in enhancing Large Language Models' (LLMs) performance on downstream tasks. However, real-world fine-tuning faces inherent conflicts between model providers' intellectual property protection, clients' data privacy requirements, and tuning costs. While recent approaches like split learning and offsite tuning demonstrate promising architectures for privacy-preserving fine-tuning, there is a gap in systematically addressing the multidimensional trade-offs required for diverse real-world deployments. We propose several indicative evaluation metrics to guide design trade-offs for privacy-preserving fine-tuning and a series of example designs, collectively named GuardedTuning; they result from novel combinations of system architectures with adapted privacy-enhancement methods and emerging computation techniques. Each design represents distinct trade-offs across model utility, privacy guarantees, and costs. Experimental results demonstrate that these designs protect against data reconstruction attacks while maintaining competitive fine-tuning performance.