IEEE Transactions on Big Data最新文献_第10页

Unified Representation Learning for Discrete Attribute Enhanced Completely Cold-Start Recommendation 离散属性统一表示学习增强完全冷启动推荐

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-04-10 DOI: 10.1109/TBDATA.2024.3387276

Haoyue Bai;Min Hou;Le Wu;Yonghui Yang;Kun Zhang;Richang Hong;Meng Wang

{"title":"Unified Representation Learning for Discrete Attribute Enhanced Completely Cold-Start Recommendation","authors":"Haoyue Bai;Min Hou;Le Wu;Yonghui Yang;Kun Zhang;Richang Hong;Meng Wang","doi":"10.1109/TBDATA.2024.3387276","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3387276","url":null,"abstract":"Recommender systems face a daunting challenge when entities (users or items) without any historical interactions, known as the “<italic>Completely Cold-Start Problem</i>”. Due to the absence of collaborative signals, Collaborative Filtering (CF) schema fails to deduce user preferences or item characteristics for such cold entities. A common solution is incorporating auxiliary discrete attributes as the bridge to spread collaborative signals to cold entities. Most previous works involve embedding collaborative signals and discrete attributes into different spaces before aligning them for information propagation. Nevertheless, we argue that the separate embedding approach disregards potential high-order similarities between two signals. Furthermore, existing alignment modules typically narrow the geometric-based distance, lacking in-depth exploration of semantic overlap between collaborative signals and cold entities. In this paper, we propose a novel discrete attribute-enhanced completely cold-start recommendation framework, which aims to improve recommendation performance by modeling heterogeneous signals in a unified space. Specifically, we first construct a heterogeneous user-item-attribute graph and capture high-order similarities between heterogeneous signals in a graph-based message-passing manner. To achieve better information alignment, we propose two self-supervised alignment modules from the semantic mutual information and user-item preference perspective. Extensive experiments on six real-world datasets in two types of discrete attribute scenarios consistently verify the effectiveness of our framework.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1091-1102"},"PeriodicalIF":7.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-Directional Transfer Graph Contrastive Learning for Social Recommendation 面向社会推荐的双向迁移图对比学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-04-10 DOI: 10.1109/TBDATA.2024.3387340

Lei Sang;Mingyuan Liu;Yi Zhang;Yuee Huang;Yiwen Zhang

{"title":"Bi-Directional Transfer Graph Contrastive Learning for Social Recommendation","authors":"Lei Sang;Mingyuan Liu;Yi Zhang;Yuee Huang;Yiwen Zhang","doi":"10.1109/TBDATA.2024.3387340","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3387340","url":null,"abstract":"Graph Neural Networks (GNNs) have emerged as an effective approach for social recommender systems. GNNs excel at capturing the graph-structured semantic information within the collaborative interaction graph and social networks. Recently, some methods have introduced self-supervised learning to GNNs, aiming to enhance recommendation performance by mitigating the data sparsity issue. However, these methods treat the interaction graph and social network as separate entities, which severely limits the number of samples available for self-supervised learning. Moreover, these separated methods also exacerbate the problem of information islands in collaborative and social domains, resulting in suboptimal performance. To tackle these challenges, we propose an innovative self-supervised social recommendation method called Bi-directional Transfer Graph Contrastive Learning (BTGCL). BTGCL jointly encodes node representations within both collaborative domain and social domain, then generates node views through feature augmentation. To bridge the information gap between domains, we devise a bi-directional migration mechanism that aligns features from the collaborative and social domains of the same positive pair. Through extensive experiments conducted on three publicly available datasets, we demonstrate the effectiveness of our proposed method in enhancing social recommendation performance.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1078-1090"},"PeriodicalIF":7.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HyFit: Hybrid Fine-Tuning With Diverse Sampling for Abstractive Summarization HyFit：混合微调与不同采样的抽象总结

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-04-10 DOI: 10.1109/TBDATA.2024.3387311

Shu Zhao;Yuanfang Cheng;Yanping Zhang;Jie Chen;Zhen Duan;Yang Sun;Xinyuan Wang

{"title":"HyFit: Hybrid Fine-Tuning With Diverse Sampling for Abstractive Summarization","authors":"Shu Zhao;Yuanfang Cheng;Yanping Zhang;Jie Chen;Zhen Duan;Yang Sun;Xinyuan Wang","doi":"10.1109/TBDATA.2024.3387311","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3387311","url":null,"abstract":"Abstractive summarization has made significant progress in recent years, which aims to generate a concise and coherent summary that contains the most important facts from the source document. Current fine-tuning approaches based on pre-training models typically rely on autoregressive and maximum likelihood estimation, which may result in inconsistent historical distributions generated during the training and inference stages, i.e., exposure bias problem. To alleviate this problem, we propose a hybrid fine-tuning model(HyFit), which combines contrastive learning and reinforcement learning in a diverse sampling space. Firstly, we introduce reparameterization and probability-based sampling methods to generate a set of summary candidates called candidates bank, which improves the diversity and quality of the decoding sampling space and incorporates the potential for uncertainty. Secondly, hybrid fine-tuning with sampled candidates bank, upweighting confident summaries and downweighting unconfident ones. Experiments demonstrate that HyFit significantly outperforms the state-of-the-art models on SAMSum and DialogSum. HyFit also shows good performance on low-resource summarization, on DialogSum dataset, using only approximate 8% of the examples exceed the performance of the base model trained on all examples.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1054-1065"},"PeriodicalIF":7.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Projected Sample Selector for Active Learning 主动学习的无监督投影样本选择器

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-30 DOI: 10.1109/TBDATA.2024.3407545

Yueyang Pi;Yiqing Shi;Shide Du;Yang Huang;Shiping Wang

{"title":"Unsupervised Projected Sample Selector for Active Learning","authors":"Yueyang Pi;Yiqing Shi;Shide Du;Yang Huang;Shiping Wang","doi":"10.1109/TBDATA.2024.3407545","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407545","url":null,"abstract":"Active learning, as a technique, aims to effectively label specific data points while operating within a designated query budget. Nevertheless, the majority of unsupervised active learning algorithms are based on shallow linear representation and lack sufficient interpretability. Furthermore, certain diversity-based methods face challenges in selecting samples that adequately represent the entire data distribution. Inspired by these reasons, in this paper, we propose an unsupervised active learning method on orthogonal projections to construct a deep neural network model. By optimizing the orthogonal projection process, we establish the connection between projection and active learning, consequently enhancing the interpretability of the proposed method. The proposed method can efficiently project the feature space onto a spanned subspace, deriving an indicator matrix while calculating the projection loss. Moreover, we consider the redundancy among samples to ensure both data point diversity and enhancement of clustering-based algorithms. Through extensive comparative experiments on six public datasets, the results demonstrate that the proposed method can effectively select more informative and representative samples and improve performance by up to 11%.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"485-498"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Communication-Efficient Distributed Learning via Sparse and Adaptive Stochastic Gradient 基于稀疏和自适应随机梯度的高效通信分布式学习

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-30 DOI: 10.1109/TBDATA.2024.3407510

Xiaoge Deng;Dongsheng Li;Tao Sun;Xicheng Lu

{"title":"Communication-Efficient Distributed Learning via Sparse and Adaptive Stochastic Gradient","authors":"Xiaoge Deng;Dongsheng Li;Tao Sun;Xicheng Lu","doi":"10.1109/TBDATA.2024.3407510","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407510","url":null,"abstract":"Gradient-based optimization methods implemented on distributed computing architectures are increasingly used to tackle large-scale machine learning applications. A key bottleneck in such distributed systems is the high communication overhead for exchanging information, such as stochastic gradients, between workers. The inherent causes of this bottleneck are the frequent communication rounds and the full model gradient transmission in every round. In this study, we present SASG, a communication-efficient distributed algorithm that enjoys the advantages of sparse communication and adaptive aggregated stochastic gradients. By dynamically determining the workers who need to communicate through an adaptive aggregation rule and sparsifying the transmitted information, the SASG algorithm reduces both the overhead of communication rounds and the number of communication bits in the distributed system. For the theoretical analysis, we introduce an important auxiliary variable and define a new Lyapunov function to prove that the communication-efficient algorithm is convergent. The convergence result is identical to the sublinear rate of stochastic gradient descent, and our result also reveals that SASG scales well with the number of distributed workers. Finally, experiments on training deep neural networks demonstrate that the proposed algorithm can significantly reduce communication overhead compared to previous methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"234-246"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

System Identification With Fourier Transformation for Long-Term Time Series Forecasting 傅立叶变换用于长期时间序列预测的系统辨识

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-30 DOI: 10.1109/TBDATA.2024.3407568

Xiaoyi Liu;Duxin Chen;Wenjia Wei;Xia Zhu;Hao Shi;Wenwu Yu

{"title":"System Identification With Fourier Transformation for Long-Term Time Series Forecasting","authors":"Xiaoyi Liu;Duxin Chen;Wenjia Wei;Xia Zhu;Hao Shi;Wenwu Yu","doi":"10.1109/TBDATA.2024.3407568","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407568","url":null,"abstract":"Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods. However, most neural network based methods fail in extracting the hidden mechanism of the targeted physical system. To overcome these shortcomings, an interpretable sparse system identification method without any prior knowledge is proposed in this study. This method adopts the Fourier transform to reduces the irrelevant items in the dictionary matrix, instead of indiscriminate usage of polynomial functions in most system identification methods. It shows an visible system representation and greatly reduces computing cost. With the adoption of <inline-formula><tex-math>$l_{1}$</tex-math></inline-formula> norm in regularizing the parameter matrix, a sparse description of the system model can be achieved. Moreover, three data sets including the water conservancy data, global temperature data and financial data are used to test the performance of the proposed method. Although no prior knowledge was known about the physical background, experimental results show that our method can achieve long-term prediction regardless of the noise and incompleteness in the original data more accurately than the widely-used baseline data-driven methods. This study may provide some insight into time-series prediction investigations, and suggests that a white-box system identification method may extract the easily overlooked yet inherent periodical features and may beat neural-network based black-box methods on long-term prediction tasks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"474-484"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate Network Alignment via Consistency in Node Evolution 基于节点演化一致性的精确网络对齐

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-30 DOI: 10.1109/TBDATA.2024.3407543

Qiyao Peng;Yinghui Wang;Pengfei Jiao;Huaming Wu;Wenjun Wang

{"title":"Accurate Network Alignment via Consistency in Node Evolution","authors":"Qiyao Peng;Yinghui Wang;Pengfei Jiao;Huaming Wu;Wenjun Wang","doi":"10.1109/TBDATA.2024.3407543","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407543","url":null,"abstract":"Network alignment, which integrates multiple network resources by identifying anchor nodes that exist in different networks, is beneficial for conducting comprehensive network analysis. Although there have been many studies on network alignment, most of them are limited to static scenarios and only can achieve acceptable top-<inline-formula><tex-math>$alpha$</tex-math></inline-formula> (<inline-formula><tex-math>$alpha > 10$</tex-math></inline-formula>) results. In the absence of considering dynamic changes in networks, accurate network alignment (i.e., top-1 result) faces two problems: 1) Missing information: focusing solely on aligning networks at a specific time leads to low top-1 performance due to the lack of information from other time periods; 2) Confusing information: ignoring temporal information and focusing on aligning networks across the entire time span leads to low top-1 performance due to inability to distinguish the neighborhood nodes of anchor nodes. In this paper, we propose a dynamic network alignment method, which aims to achieve better top-1 alignment results with consider changing network structures over time. Towards this end, we learn the representations of nodes in the changing network structure with time, and preserve the consistency of anchor node pairs during the time-evolution process. First, we employ a Structure-Time-aware module to capture network dynamics while preserving network structure and learning node representations that incorporate temporal information. Second, we ensure the global and local consistency of anchor node pairs over time by utilizing linear and similarity functions, respectively. Finally, we determine whether two nodes are anchor node pairs by maintaining consistency between global, local, and node representations. Experimental results obtained from real-world datasets demonstrate that the proposed model achieves performance comparable to several state-of-the-art methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"499-511"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification CompanyKG：公司相似性量化的大规模异质图

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-30 DOI: 10.1109/TBDATA.2024.3407573

Lele Cao;Vilhelm von Ehrenheim;Mark Granroth-Wilding;Richard Anselmo Stahl;Andrew McCornack;Armin Catovic;Dhiana Deva Cavalcanti Rocha

{"title":"CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification","authors":"Lele Cao;Vilhelm von Ehrenheim;Mark Granroth-Wilding;Richard Anselmo Stahl;Andrew McCornack;Armin Catovic;Dhiana Deva Cavalcanti Rocha","doi":"10.1109/TBDATA.2024.3407573","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3407573","url":null,"abstract":"In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis, and mergers and acquisitions. We propose and publish a knowledge graph, named CompanyKG, to represent and learn diverse company features and relations. Specifically, 1.17 million companies are represented as nodes enriched with company description embeddings; and 15 different inter-company relations result in 51.06 million weighted edges. To enable a comprehensive assessment of methods for company similarity quantification, we have devised and compiled three evaluation tasks with annotated test sets: similarity prediction, competitor retrieval and similarity ranking. We present extensive benchmarking results for 11 reproducible predictive methods categorized into three groups: node-only, edge-only, and node+edge. To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"247-258"},"PeriodicalIF":7.5,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces 深本影：城市空间阳光获取计算的生成方法

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-28 DOI: 10.1109/TBDATA.2024.3382964

Kazi Shahrukh Omar;Gustavo Moreira;Daniel Hodczak;Maryam Hosseini;Nicola Colaninno;Marcos Lage;Fabio Miranda

{"title":"Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces","authors":"Kazi Shahrukh Omar;Gustavo Moreira;Daniel Hodczak;Maryam Hosseini;Nicola Colaninno;Marcos Lage;Fabio Miranda","doi":"10.1109/TBDATA.2024.3382964","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3382964","url":null,"abstract":"Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. While access to sunlight is essential to the success of urban environments, shadows can provide shaded places to stay during the hot seasons, mitigate heat island effect, and increase pedestrian comfort levels. Properly quantifying sunlight access and shadows in large urban environments is key in tackling some of the important challenges facing cities today. In this paper, we propose Deep Umbra, a novel computational framework that enables the quantification of sunlight access and shadows at a global scale. Our framework is based on a conditional generative adversarial network that considers the physical form of cities to compute high-resolution spatial information of accumulated sunlight access for the different seasons of the year. We use data from seven different cities to train our model, and show, through an extensive set of experiments, its low overall RMSE (below 0.1) as well as its extensibility to cities that were not part of the training set. Additionally, we contribute a set of case studies and a comprehensive dataset with sunlight access information for more than 100 cities across six continents of the world.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 2","pages":"388-401"},"PeriodicalIF":7.5,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10483268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3A Multi-Classification Division-Aggregation Framework for Fake News Detection 虚假新闻检测的多分类划分聚合框架

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-03-26 DOI: 10.1109/TBDATA.2024.3378098

Wen Zhang;Haitao Fu;Huan Wang;Zhiguo Gong;Pan Zhou;Di Wang

{"title":"3A Multi-Classification Division-Aggregation Framework for Fake News Detection","authors":"Wen Zhang;Haitao Fu;Huan Wang;Zhiguo Gong;Pan Zhou;Di Wang","doi":"10.1109/TBDATA.2024.3378098","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3378098","url":null,"abstract":"Nowadays, as human activities are shifting to social media, fake news detection has been a crucial problem. Existing methods ignore the classification difference in online news and cannot take full advantage of multi-classification knowledges. For example, when coping with a post “A mouse is frightened by a cat,” a model that learns “computer” knowledge tends to misunderstand “mouse” and give a fake label, but a model that learns “animal” knowledge tends to give a true label. Therefore, this research proposes a multi-classification division-aggregation framework to detect fake news, named <inline-formula><tex-math>$CKA$</tex-math></inline-formula>, which innovatively learns classification knowledges during training stages and aggregates them during prediction stages. It consists of three main components: a news characterizer, an ensemble coordinator, and a truth predictor. The news characterizer is responsible for extracting news features and obtaining news classifications. Cooperating with the news characterizer, the ensemble coordinator generates classification-specifical models for the maximum reservation of classification knowledges during the training stage, where each classification-specifical model maximizes the detection performance of fake news on corresponding news classifications. Further, to aggregate the classification knowledges during the prediction stage, the truth predictor uses the truth discovery technology to aggregate the predictions from different classification-specifical models based on reliability evaluation of classification-specifical models. Extensive experiments prove that our proposed <inline-formula><tex-math>$CKA$</tex-math></inline-formula> outperforms state-of-the-art baselines in fake news detection.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"130-140"},"PeriodicalIF":7.5,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0