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检索条件"任意字段=11th ACM International Conference on Web Search and Data Mining"

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18th acm international conference on web search and data mining, WSDM 2025

作者： Karanth, Pallavi TIB Leibniz Information Centre for Science and Technology Curation and Community Building Hannover Germany

ISBN: (纸本)9798400713293

Large datasets in silos are available to be harnessed for potential benefits in terms of insights for better recommendations, search and clustering. Healthcare datasets include health care professionals' (HCP) data about their affiliations, publications, participation in conferences, events, clinical trials, specialty and much more. Such datasets in silos are integrated based on the different dimensions such as specialty, participation in various healthcare related events and clinical trials. Integration of healthcare related datasets provide various benefits like better search results, better recommendations of healthcare professionals based on their specialty and needs of patients and better insights to determine key opinion leaders in various therapy areas. In this work, we apply the Sigmoid similarity algorithm to find similar health care professionals based on the HCP Ontology we have developed. Sigmoid similarity is a feature based semantic similarity measure which outperforms the other hierarchy based approaches for computing semantic similarity. this semantic similarity measure enables us to compare and evaluate the semantic similarity amongst various health care professionals to accurately retrieve and recommend healthcare professionals based on the context of search. © 2025 Copyright is held by the owner/author(s).

关键词： Electronic health record

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Towards Ethical and Personalized web Navigation Agents: A Framework for User-Aligned Task Execution 25

Towards Ethical and Personalized Web Navigation Agents: A Fr...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Dammu, Preetam Prabhu Srikar University of Washington SeattleWA United States

ISBN: (纸本)9798400713293

Generative AI has advanced the capabilities of autonomous agents, enabling autonomous execution of complex web navigation tasks that can reshape digital interactions across various domains. Yet, to reach their full potential, these agents must be ethically aligned and personalized to individual user needs-a challenge complicated by privacy concerns and the risk of reinforcing biases. this work introduces a novel framework that enables responsible, user-guided personalization of web navigation agents, ensuring alignment with ethical standards and user preferences. By developing agents capable of perceiving, reasoning, and adapting in alignment with user preferences, this work proposes an approach that transcends generic task execution. Employing a structured representation of user-specific tasks, the agent utilizes interactive and reasoning actions to personalize workflows, adapting responsively to individual contexts. Evaluation through task success metrics and user satisfaction scores further assesses the ethical alignment and utility of personalized interactions. this research lays the groundwork for responsible agents that offer personalized assistance while adhering to ethical and privacy standards, with implications for information retrieval, e-commerce, and other knowledge-intensive applications. © 2025 Copyright held by the owner/author(s).

关键词： Differential privacy

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Advances in Vector search 25

Advances in Vector Search

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18th acm international conference on web search and data mining, WSDM 2025

作者： Bruch, Sebastian Northeastern University BostonMA United States

ISBN: (纸本)9798400713293

Whether a text document is freed from the rules of grammar, stripped of word order, and thereby turned into a bag of words, or whether its semantic nuances learnt and condensed into an embedding space, its final representation is the same mathematical object: a vector. In fact, vectors represent much more than just text documents. Any object, be it a document or query, that contains text, images, speech, or a mix of these modalities, is often represented as a vector. Collect a large enough quantity of these vectors and the fundamental question of retrieval from the Information Retrieval (IR) discipline becomes urgently relevant: Finding k vectors that are more similar to a query. this full-day tutorial is concerned with the question above and intends to cover foundational concepts and advanced algorithms for vector retrieval or vector search. the tutorial begins with a focus on foundational concepts, including a brief history from space partitioning, to locality-sensitive hashing, graph-based, and clustering-based methods. As we discuss each class of solutions, we show failure scenarios and explain why they prove insufficient. We conclude the tutorial by turning our attention in the second half to recent developments for maximum inner product search over dense and sparse vectors, as well as open questions that need further research. through this tutorial, we wish to recap the fascinating topic of retrieval in modern IR for the community, lower barriers of entry into this rich area of research, and inspire interest in conducting research on the underlying theoretical and empirical questions that are specific to IR. © 2025 Copyright held by the owner/author(s).

关键词： Semantics

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DDualSE: Decoupled Dual-head Squeeze and Excitation Attention for Sequential Recommendation 25

DDualSE: Decoupled Dual-head Squeeze and Excitation Attentio...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Mo, Nijia Zang, Jianxiang Wang, Zhan Liu, Hui School of Statistics and Information Shanghai University of International Business and Economics Shanghai China

ISBN: (纸本)9798400713293

Sequential recommendation is one of the key tasks in recommendation systems. Current approaches employed context-aware models with enhanced expressive capabilities, typically incorporating input with temporal and spatial information - specifically, historical interaction items and their attributes. However, these models often represented the input as a 1D or 2D matrix, performing coarse-grained fusion of item attributes. this approach did not explicitly model the relationships between spatial and temporal information, consequently impeding the model's ability to acquire intricate local details. In this study, we propose the Decoupled Dual-headed Squeeze and Excitation attention module (DDualSE), which constructs a fine-grained 3D input by separately modeling temporal and spatial information. Additionally, DDualSE decouples embedding dimensions and the number of attention heads from both the sequence length and the number of attributes simultaneously, solving the low-rank bottleneck issue without introducing extra parameters or specifying additional hyper-parameters. We integrate DDualSE into BERT and conduct extensive experiments on three benchmark datasets to validate the effectiveness of the proposed model. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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WSDM 2020 - Proceedings of the 13th international conference on web search and data mining

WSDM 2020 - Proceedings of the 13th International Conference...

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13th acm international conference on web search and data mining, WSDM 2020

ISBN: (纸本)9781450368223

the proceedings contain 126 papers. the topics discussed include: extreme regression for dynamic search advertising;from missing data to Boltzmann distributions and time dynamics: the statistical physics of recommendation;computer vision for fashion: from individual recommendations to world-wide trends;veridical data science;can deep learning only be neural networks?;recurrent attention walk for semi-supervised classification;outlier resistant unsupervised deep architectures for attributed network embedding;privacy-aware recommendation with private-attribute protection using adversarial learning;and LouvainNE: hierarchical Louvain method for high quality and scalable network embedding.

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Density-aware and Cluster-based Federated Anomaly Detection on data Streams 25

Density-aware and Cluster-based Federated Anomaly Detection ...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Li, Bin Cheng, Li Qin, Zheng Wu, Yunlong Beijing China National University of Defense Technology College of System Engineering Changsha China Academy of Military Sciences Defense Innovation Institute Beijing China

ISBN: (纸本)9798400713293

Federated active anomaly detection on data streams becomes a crucial research problem, since it attempts to discover anomalous data with protecting data privacy and avoiding extensive data labeling. Although extensive work has been conducted on anomaly detection, distinguishing similar anomalies of different categories still remains quite a challenging issue. the requirement of privacy protection in federated settings aggravates the difficulties for instance query and scoring in active anomaly detection when solving this issue. To the best of our knowledge, limited work has focused on this research area. therefore, we propose Density-aware and cluster-based Federated Active anomaly detection on data Streams, called DFAS. We design a novel lightweight federated anomaly detection clusters with density-aware hash cells, which successfully capture evolving data distribution. the federated anomaly detection clusters are incrementally updated with an acceptable theoretical reconstruction error guarantee. In addition, we propose a straightforward but effective metric divergences accompanied by a greedy search algorithm, which takes both global aggregation bias mitigation and efficiency into account. At last, DFAS detects anomalies and queries the instances for manual labels by measuring the density in hash cells of each cluster, effectively distinguishing closely distributed anomaly classes while maintaining data privacy in the federated setting. Comprehensive experiments on several real-world data sets show that DFAS outperforms previous methods, improving F1 scores by up to 26.7%. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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RSM: Reinforced Subgraph Matching Framework with Fine-grained Operation based search Plan 25

RSM: Reinforced Subgraph Matching Framework with Fine-graine...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Li, Ziming Dou, Yuequn Li, Youhuan Chen, Xinhuan Zhang, Chuxu College of Computer Science and Electronic Engineering Hunan University Changsha China Tencent Inc. Shenzhen China University of Connecticut Connecticut United States

ISBN: (纸本)9798400713293

Subgraph matching is one of the fundamental problems in graph analytics. Existing methods generate matching orders to guide their search, which consists of a series of extensions. Each time, they extend smaller partial matches into larger ones until all complete answers are obtained. However, these methods have two significant drawbacks. Firstly, their matching order generations are usually heuristic and challenging to be effective for different queries. Secondly, each extension, serving as its computation unit, is coarse-grained and may hinder performance. this granularity issue stems from merging generation and expansion operations into a single computation unit. To address these challenges, we introduce a pioneering framework for Reinforced Subgraph Matching (RSM) that features a fine-grained operation-based search plan. Initially, RSM proposes a fresh paradigm for search, referred to as operation-level search, where each computation unit is defined as an operation that either generates or expands a candidate set under a query vertex. To deal with the second problem and fully exploit the potential of this novel search paradigm, RSM implements a reinforcement learning strategy to generate operation-level search plans. RSM's reinforcement learning approach for constructing operation-based search plans encompasses three modules. In the first module, we employ graph neural networks to extract query vertex representation from graphs. then, the other two modules leverage multilayer perceptron and are designed to create the generation and expansion operations, respectively. Extensive experiments on real-world graph datasets validate that RSM cuts down query processing time, outperforming existing algorithms by up to 1 to 2 orders of magnitude. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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MedTransTab: Advancing Medical Cross-Table Tabular data Generation 25

MedTransTab: Advancing Medical Cross-Table Tabular Data Gene...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Chen, Yuyan Guo, Qingpei You, Shuangjie Li, Zhixu Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Ant Group Beijing China Georgia Institute of Technology AtlantaGA United States Renmin University of China Suzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798400713293

In medical research, clinical trials are pivotal. While prospective clinical research provides a systematic approach to collecting patient data, it grapples with challenges like long durations, increased costs, and most crucially, data scarcity. To address above-mentioned challenge, this paper introduces a novel approach: using cross-table generation to create relevant data. Unlike existing work focused on single-table operations, our method leverages data from multiple sources across various tables, integrating diverse data types and ensuring data consistency across multiple tables. We develop a new framework, MedTransTab, tailored for cross-table tabular data generation in the medical context. this framework extends our previous efforts and is built upon the newly constructed PMC-Struct, derived from an unstructured PMC-patient dataset. Our MedTransTab can generate high-quality patient records, synthesizing detailed biomedical information to align with real or simulated tables from multiple sources. the experiments show that the proposed method significantly improves performance in cross-table tasks. On the PMC-Struct-Plus dataset, we observe an average improvement of 28.85% in data generation and prediction. Similarly, on the Out-Of-Domain (OOD) dataset, there's an average improvement of 22.56%, indicating substantial progress in medical data analysis. © 2025 Copyright held by the owner/author(s).

关键词： data consistency

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Maintaining k-MinHash Signatures over Fully-Dynamic data Streams with Recovery 25

Maintaining k-MinHash Signatures over Fully-Dynamic Data Str...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Clementi, Andrea Gualà, Luciano Pepè Sciarria, Luca Straziota, Alessandro University of Rome "Tor Vergata" Department of Enterprise Engineering Rome Italy

ISBN: (纸本)9798400713293

We consider the task of performing Jaccard similarity queries over a large collection of items that are dynamically updated according to a streaming input model. An item here is a subset of a large universe U of elements. A well-studied approach to address this important problem in data mining is to design fast-similarity data sketches. In this paper, we focus on global solutions for this problem, i.e., a single data structure which is able to answer both Similarity Estimation and All-Candidate Pairs queries, while also dynamically managing an arbitrary, online sequence of element insertions and deletions received in input. We introduce and provide an in-depth analysis of a dynamic, buffered version of the well-known k-MinHash sketch. this buffered version better manages critical update operations thus significantly reducing the number of times the sketch needs to be rebuilt from scratch using expensive recovery queries. We prove that the buffered k-MinHash uses O(k log |U |) memory words per subset and that its amortized update time per insertion/deletion is O(k log |U |) with high probability. Moreover, our data structure can return the k-MinHash signature of any subset in O(k) time, and this signature is exactly the same signature that would be computed from scratch (and thus the quality of the signature is the same as the one guaranteed by the static k-MinHash). Analytical and experimental comparisons with the other, state-ofthe-art global solutions for this problem given in [Bury et al.,WSDM'18] show that the buffered k-MinHash turns out to be competitive in a wide and relevant range of the online input parameters. © 2025 Copyright held by the owner/author(s).

关键词： data streams

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Adjacent Neighborhood Transformer-based Diffusion Model for Anomaly Detection under Incomplete Industrial data Sources 25

Adjacent Neighborhood Transformer-based Diffusion Model for ...

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18th acm international conference on web search and data mining, WSDM 2025

作者： Wang, Lulu Li, Chengqing School of Mathematics and Computational Science Xiangtan University Xiangtan China School of Computer Science Xiangtan University Xiangtan China

ISBN: (纸本)9798400713293

Anomaly detection in an industrial setting is crucial for operational monitoring, yet it remains challenging under incomplete data conditions. Common issues, such as sensor failures, data transmission loss, and storage malfunctions, often result in missing data, complicating the detection of anomalies, particularly when these are localized within specific regions. To address the challenges, this paper proposes DiffANT, an unsupervised anomaly detection method that integrates a diffusion model with the Adjacent Neighborhood Transformer (ANT). Specifically, DiffANT begins by applying various data masking techniques to simulate realistic missing values that reflect real-world industrial scenarios. the ANT utilizes a Transformer encoder architecture augmented with an adjacent neighborhood attention mechanism. It effectively focuses on relevant non-immediate vicinities to enhance anomaly detection. DiffANT then reconstructs the original data from randomly sampled noise through diffusion and denoising processes and utilizes a multi-level reconstruction strategy to refine the generated samples. We demonstrate the efficacy of DiffANT through extensive experiments in diverse industrial applications, such as secure water treatment and server machine monitoring. the results indicate that DiffANT consistently outperforms state-of-the-art methods in detecting anomalies in time series data, regardless of whether the data is incomplete. © 2025 Copyright held by the owner/author(s).

关键词： Time series

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