OpenRaiser/Intern-Atlas huggingface dataset:
Behavior Modeling Space Reconstruction for E-Commerce Search
Authors: Yejing Wang, Chi Zhang, Xiangyu Zhao, Qidong Liu, Maolin Wang, Xuetao Wei, Zitao Liu, Xing Shi, Xudong Yang, Ling Zhong, Wei Lin
Venue: WWW
Year: 2025

Delivering superior search services is crucial for enhancing customer experience and driving revenue growth in e-commerce. Conventionally, search systems model user behaviors by combining user preference and query-item relevance statically, often through a fixed logical 'and' relationship. This paper reexamines existing approaches through a unified lens using causal graphs and Venn diagrams, uncovering two prevalent yet significant issues: entangled preference and relevance effects, and a collapsed modeling space. To surmount these challenges, our research introduces a novel framework, DRP, which enhances search accuracy through two components to reconstruct the behavior modeling space. Specifically, we implement preference editing to proactively remove the relevance effect from preference predictions, yielding untainted user preferences. Additionally, we employ adaptive fusion, which dynamically adjusts fusion criteria to align with the varying patterns of relevance and preference, facilitating more nuanced and tailored behavior predictions within the reconstructed modeling space. Empirical validation on two public datasets and a proprietary e-commerce search dataset underscores the superiority of our proposed methodology, demonstrating marked improvements in performance over existing approaches. The code is available at https://github.com/Applied-Machine-Learning-Lab/DRP.
Inferentially-Private Private Information
Authors: Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
Venue: WWW
Year: 2025

Information disclosure can compromise privacy when revealed information is correlated with private information. We consider the notion of inferential privacy, which measures privacy leakage by bounding the inferential power a Bayesian adversary can gain by observing a released signal. Our goal is to devise an inferentially-private private information structure that maximizes the informativeness of the released signal, following the Blackwell ordering principle, while adhering to inferential privacy constraints. To achieve this, we devise an efficient release mechanism that achieves the inferentially-private Blackwell optimal private information structure for the setting where the private information is binary. Additionally, we propose a programming approach to compute the optimal structure for general cases given the utility function. The design of our mechanisms builds on our geometric characterization of the Blackwell-optimal disclosure mechanisms under privacy constraints, which may be of independent interest.
A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
Authors: Zihan Wang, Ziqi Zhao, Yougang Lyu, Zhumin Chen, Maarten de Rijke, Zhaochun Ren
Venue: WWW
Year: 2025

Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. This task presents substantial challenges due to minimal human intervention. Recent work has adapted large language models (LLMs) for zero-shot NER by crafting specialized prompt templates. And it advances models' self-learning abilities by incorporating self-annotated demonstrations. Two important challenges persist: (i) Correlations between contexts surrounding entities are overlooked, leading to wrong type predictions or entity omissions. (ii) The indiscriminate use of task demonstrations, retrieved through shallow similarity-based strategies, severely misleads LLMs during inference. In this paper, we introduce the cooperative multi-agent system (CMAS), a novel framework for zero-shot NER that uses the collective intelligence of multiple agents to address the challenges outlined above. CMAS has four main agents: (i) a self-annotator, (ii) a type-related feature (TRF) extractor, (iii) a demonstration discriminator, and (iv) an overall predictor. To explicitly capture correlations between contexts surrounding entities, CMAS reformulates NER into two subtasks: recognizing named entities and identifying entity type-related features within the target sentence. To enable controllable utilization of demonstrations, a demonstration discriminator is established to incorporate the self-reflection mechanism, automatically evaluating helpfulness scores for the target sentence. Experimental results show that CMAS significantly improves zero-shot NER performance across six benchmarks, including both domain-specific and general-domain scenarios. Furthermore, CMAS demonstrates its effectiveness in few-shot settings and with various LLM backbones.
EKD4Rec: Ensemble Knowledge Distillation from LLM-based Models to Traditional Sequential Recommenders
Authors: Yue Wang, Dingyi Zhang, Haoyu Wenren, Yue Wang, Yingming Li
Venue: WWW
Year: 2025

In this paper, we propose a new ensemble knowledge distillation method for distilling knowledge from LLM-based recommendation (teacher) models to traditional light-weight sequential recommendation (student) models. In particular, instead of using one single teacher model, the averaged prediction from multiple teachers is employed as the soft targets of knowledge distillation. Further, only the top K soft labels of teachers' output distribution are sampled for distillation to make it more focused on the corresponding high-ranked items. Extensive experiments on three public datasets show the effectiveness of the proposed ensemble knowledge distillation for sequential recommendation (EKD4Rec).
X-ClusterLink: An Efficient Cross-Cluster Communication Framework in Multi-Kubernetes Clusters
Authors: Pengbo Wang, Gongming Zhao, Yuantao Wu, Hongli Xu, Haibo Wang
Venue: WWW
Year: 2025

Kubernetes is widely adopted by enterprises to enhance service availability for applications such as web services and large-scale model training, due to its advantages in managing containerized applications. As service demands increase, a single Kubernetes cluster often becomes insufficient, leading to the trend of using multiple clusters to improve service scalability. However, achieving efficient cross-cluster communication poses significant challenges due to the need for low latency, high throughput, and strong robustness. Existing methods for cross-cluster communication either employ a centralized control plane, which becomes a communication bottleneck, or use numerous service-bound proxies, leading to increased management complexity and possibly compromised robustness in cross-cluster communication. To address the above challenges, we introduce X-ClusterLink, a framework designed for efficient cross-cluster communication in multi-Kubernetes clusters. X-ClusterLink first employs broker clusters to ensure low-latency cross-cluster synchronization. Then, it aggregates multiple containerized gateways to enhance throughput and leverages eXpress Data Path (XDP) for advanced packet processing, thereby accelerating traffic forwarding. Finally, it incorporates Bucket-Based Consistent ECMP to facilitate seamless failover and enhance robustness. Experimental results demonstrate that X-ClusterLink significantly improves cross-cluster communication efficiency, increasing cross-cluster forwarding bandwidth by 3.1 × compared to existing solutions.
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
Authors: Chengbing Wang, Yang Zhang, Fengbin Zhu, Jizhi Zhang, Tianhao Shi, Fuli Feng
Venue: WWW
Year: 2025

Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, the limited context window of LLMs often restricts them to focusing on recent user interactions only, leading to the neglect of long-term interests involved in the longer histories. To address this challenge, we propose a novel Automatic Memory-Retrieval framework ( AutoMR ), which is capable of storing long-term interests in the memory and extracting relevant information from it for next-item generation within LLMs. Extensive experimental results on two real-world datasets demonstrate the effectiveness of our proposed AutoMR framework in utilizing long-term interests for generative recommendation.
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
Authors: Zixiang Wang, Yinghao Zhu, Huiya Zhao, Xiaochen Zheng, Dehao Sui, Tianlong Wang, Wen Tang, Yasha Wang, Ewen M. Harrison, Chengwei Pan, Junyi Gao, Liantao Ma
Venue: WWW
Year: 2025

We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our approach seamlessly integrates domain-specific expert models with LLMs to bridge the gap between structured EHR data and text-based reasoning. Inspired by the Multidisciplinary Team (MDT) approach used in clinical settings, ColaCare employs two types of agents: DoctorAgents and a MetaAgent, which collaboratively analyze patient data. Expert models process and generate predictions from numerical EHR data, while LLM agents produce reasoning references and decision-making reports within the MDT-driven collaborative consultation framework. The MetaAgent orchestrates the discussion, facilitating consultations and evidence-based debates among DoctorAgents, simulating diverse expertise in clinical decision-making. We additionally incorporate the Merck Manual of Diagnosis and Therapy (MSD) medical guideline within a retrieval-augmented generation (RAG) module for medical evidence support, addressing the challenge of knowledge currency. Extensive experiments conducted on three EHR datasets demonstrate ColaCare's superior performance in clinical mortality outcome and readmission prediction tasks, underscoring its potential to revolutionize clinical decision support systems and advance personalized precision medicine. All code, case studies and a questionnaire are available at the project website: https://colacare.netlify.app.
Dialectal Bias in Bengali: An Evaluation of Multilingual Large Language Models Across Cultural Variations
Authors: Azmine Toushik Wasi, Raima Islam, Mst Rafia Islam, Farig Sadeque, Taki Hasan Rafi, Dong-Kyu Chae
Venue: WWW
Year: 2025

Large Language Models (LLMs) have transformed human-centric AI applications on the Web, yet they often exhibit stereotypes and biases, especially in sensitive contexts like cultural differences in low-resource languages such as Bengali. In this work, we investigate cultural bias in LLMs by evaluating their performance in Bengali cultural dialects of Hindu and Muslim majority. We evaluated widely used Web-enabled models, including ChatGPT, Gemini, and Microsoft Copilot, using a curated data set to analyze their handling of culturally specific terms and approaches to mitigating social biases. By addressing bias in language technologies that underpin the modern Web, our study contributes to advancing human-centered NLP and LLM auditing. Through a detailed exploration of bias causes and evaluation methods, our goal is to promote fairness and inclusion for more than 300 million Bengali speakers in the evolving ecosystem of the Web.
Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs
Authors: Hiroki Watanabe, Motonobu Uchikoshi
Venue: WWW
Year: 2025

Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
Path-LLM: A Multi-Modal Path Representation Learning by Aligning and Fusing with Large Language Models
Authors: Yongfu Wei, Yan Lin, Hongfan Gao, Ronghui Xu, Sean Bin Yang, Jilin Hu
Venue: WWW
Year: 2025

The advancement of intelligent transportation systems has led to a growing demand for accurate path representations, which are essential for tasks such as travel time estimation, path ranking, and trajectory analysis. However, traditional path representation learning (PRL) methods often focus solely on single-modal road network data, overlooking important physical and regional factors that influence real-world traffic dynamics. To overcome this limitation, we introduce Path-LLM, a multi-modal path representation learning model that integrates large language models (LLMs) into PRL. Our approach leverages LLMs to interpret both topological and textual data, enabling robust multi-modal path representations. To effectively align and merge these modalities, we propose TPalign, a contrastive learning-based pretraining strategy that ensures alignment within the embedding space. We then present TPfusion, a multimodal fusion module that dynamically adjusts the weight of each modality before integration. To further optimize LLM training, we introduce a Two-stage Overlapping Curriculum Learning (TOCL) approach, which progressively increases the complexity of the training data. Finally, we evaluate Path-LLM on three real-world datasets across traditional PRL downstream tasks, achieving up to a 61.84% improvement in path ranking performance on the Xi'an dataset. Additionally, Path-LLM demonstrates superior performance in both few-shot and zero-shot learning scenarios. Our code is available at: https://github.com/decisionintelligence/Path-LLM.
Ideation: Diversifying Ad Text Generation in Sponsored Search with Latent Diffusion
Authors: Penghui Wei
Venue: WWW
Year: 2025

In a sponsored search system, high-quality ad creatives (such as texts and images) that can attract users play a crucial role in improving user engagement. This paper focuses on designing approaches to automatically generate ad texts, and the goals are to 1) improve the diversity of text creatives and 2) ensure the relevance between text creatives and search queries/keywords, which are the two key factors affecting user behaviors. We propose a framework named Ideation, which generates diverse ad texts given search queries/keywords and ad landing page information with a latent diffusion based sequence-to-sequence model. Experiments on a public industrial dataset for benchmarking ad text generation show that Ideation significantly outperforms previous approaches and possesses the ability of generating fluent and diverse text creatives.
Faces Speak Louder Than Words: Emotions Versus Textual Sentiment in the 2024 USA Presidential Election
Authors: Chiyu Wei, Sean Noh, Ho-Chun Herbert Chang
Venue: WWW
Year: 2025

Sentiment analysis of textual content has become a well-established solution for analyzing social media data. However, with the rise of images and videos as primary modes of expression, more information on social media is conveyed visually. Among these, facial expressions serve as one of the most direct indicators of emotional content in images. This study analyzes a dataset of Instagram posts related to the 2024 U.S. presidential election, spanning April 5, 2024, to August 9, 2024, to compare the relationship between textual and facial sentiment. Our findings reveal that facial expressions align with text sentiment, where positive sentiment aligns with happiness, although neutral and negative facial expressions provide critical information beyond negative valence. Furthermore, during politically significant events such as Donald Trump's conviction and assassination attempt, posts depicting Trump showed a 12% increase in negative sentiment. Crucially, Democrats use their opponent's fear to depict weakness, whereas Republicans use their candidate's anger to depict resilience. Our research highlights the potential of integrating facial expression analysis with textual sentiment analysis to uncover deeper insights into social media dynamics.
Instruction Vulnerability Prediction for WebAssembly with Semantic Enhanced Code Property Graph
Authors: Bao Wen, Jingjing Gu, Hao Han, Pengfei Yu, Yang Liu
Venue: WWW
Year: 2025

WebAssembly (Wasm) is a universal low-level bytecode designed to build modern web systems. Recent studies have shown that technologies such as voltage scaling and RowHammer attacks are expected to increase the likelihood of bit flips, which may cause unacceptable or catastrophic system failures. This raises concerns about the impact of bit flips on Wasm programs, which run as instructions in web systems, and it is an undeveloped topic since the features of Wasm differ from traditional programs. In this paper, we propose a novel paradigm, namely IVPSEG, to understand the error propagation of bit flips within Wasm programs. Specifically, we first use Large Language Models (LLMs) to automatically extract instruction embeddings containing semantic knowledge of each instruction's context. Then, we exploit these embeddings and program structure (control execution and data transfer) to construct a semantic enhanced code property graph, which implicates the potential path of error propagation. Based on this graph, we utilize graph neural networks and attention diffusion to optimize instruction embeddings by capturing different error propagation patterns for instruction vulnerability prediction. In particular, we build a Wasm compilation and fault generation system to simulate bit flips at Wasm runtime. Our experimental results with 14 benchmark programs and test cases show IVPSEG outperforms the state-of-the-art methods in terms of accuracy (average 13.06%ͽ ), F1-score (average 14.93%↑), and model robustness.
Scenario-independent Uncertainty Estimation for LLM-based Question Answering via Factor Analysis
Authors: Zhihua Wen, Zhizhao Liu, Zhiliang Tian, Shilong Pan, Zhen Huang, Dongsheng Li, Minlie Huang
Venue: WWW
Year: 2025

Large language models (LLMs) demonstrate significant potential in various applications; however, they are susceptible to generating hallucinations, which can lead to the spread of online misinformation. Existing studies address hallucination detection by (1) employing reference-based methods that consult external resources for verification or (2) utilizing reference-free methods that mainly estimate answer uncertainty based on LLM's internal states. However, reference-based methods incur significant costs and can be infeasible for obtaining reliable external references. Besides, existing uncertainty estimation (UE) methods often overlook the impact of scenario backgrounds inherited from the query's lexical resources, leading to noise in UE. In almost all real-world applications, users care about the uncertainty concerning semantics or facts instead of the query's scenario information. Therefore, we argue that mitigating scenario-related noise and focusing on semantic information can yield a more desirable UE. In this paper, we introduce a plug-and-play scenario-independent framework to enhance unsupervised UE in LLMs by removing scenario-related noise and focusing on semantic information. This framework is compatible with most existing UE methods, as it leverages only the existing UE methods' outputs. Specifically, we design a scenario-specific sampling to paraphrase queries, maintaining their common semantics while diversifying the scenario distribution. Subsequently, to estimate the contribution of the common semantics, we design a factor analysis (FA) model to disentangle the UE score obtained from the given UE method into a combination of multiple latent factors, which represent the contribution of the common semantics and scenario-related noise. By solving the FA model, we decompose the impact of the most significant factor to approximate the uncertainty caused by the common semantics, thus achieving scenario-independent UE. Extensive experiments and analysis across multiple models and datasets demonstrate the effectiveness of our approach.
The 3rd Workshop on AI Agent for Information Retrieval: Generating and Ranking
Authors: Qingsong Wen, Yongfeng Zhang, Zhiwei Liu, Julian J. McAuley, Hua Wei, Linsey Pang, Wei Liu, Philip S. Yu
Venue: WWW
Year: 2025

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers and practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research and emerging trends, and foster knowledge exchange and collaboration within the community.
Less is More: Simplifying Network Traffic Classification Leveraging RFCs
Authors: Nimesha Wickramasinghe, Arash Shaghaghi, Elena Ferrari, Sanjay K. Jha
Venue: WWW
Year: 2025

The rapid growth of encryption has significantly enhanced privacy and security while posing challenges for network traffic classification. Recent approaches address these challenges by transforming network traffic into text or image formats to leverage deep-learning models originally designed for natural language processing, and computer vision. However, these transformations often contradict network protocol specifications, introduce noisy features, and result in resource-intensive processes. To overcome these limitations, we propose NetMatrix, a minimalistic tabular representation of network traffic that eliminates noisy attributes and focuses on meaningful features leveraging RFCs (Request for Comments) definitions. By combining NetMatrix with a vanilla XGBoost classifier, we implement a lightweight approach, LiM ("Less is More") that achieves classification performance on par with state-of-the-art methods such as ET-BERT and YaTC. Compared to selected baselines, experimental evaluations demonstrate that LiM improves resource consumption by orders of magnitude. Overall, this study underscores the effectiveness of simplicity in traffic representation and machine learning model selection, paving the way towards resource-efficient network traffic classification.