IEEE Transactions on Knowledge and Data Engineering最新文献

TokenRec: Learning to Tokenize ID for LLM-Based Generative Recommendations TokenRec：学习为基于llm的生成式推荐标记ID

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-19 DOI: 10.1109/TKDE.2025.3599265

Haohao Qu;Wenqi Fan;Zihuai Zhao;Qing Li

{"title":"TokenRec: Learning to Tokenize ID for LLM-Based Generative Recommendations","authors":"Haohao Qu;Wenqi Fan;Zihuai Zhao;Qing Li","doi":"10.1109/TKDE.2025.3599265","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3599265","url":null,"abstract":"There is a growing interest in utilizing large language models (LLMs) to advance next-generation Recommender Systems (RecSys), driven by their outstanding language understanding and reasoning capabilities. In this scenario, tokenizing users and items becomes essential for ensuring seamless alignment of LLMs with recommendations. While studies have made progress in representing users and items using textual contents or latent representations, challenges remain in capturing high-order collaborative knowledge into discrete tokens compatible with LLMs and generalizing to unseen users/items. To address these challenges, we propose a novel framework called <bold>TokenRec, which introduces an effective ID tokenization strategy and an efficient retrieval paradigm for LLM-based recommendations. Our tokenization strategy involves quantizing the masked user/item representations learned from collaborative filtering into discrete tokens, thus achieving smooth incorporation of high-order collaborative knowledge and generalizable tokenization of users and items for LLM-based RecSys. Meanwhile, our generative retrieval paradigm is designed to efficiently recommend top-K items for users, eliminating the need for the time-consuming auto-regressive decoding and beam search processes used by LLMs, thus significantly reducing inference time. Comprehensive experiments validate the effectiveness of the proposed methods, demonstrating that TokenRec outperforms competitive benchmarks, including both traditional recommender systems and emerging LLM-based recommender systems.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6216-6231"},"PeriodicalIF":10.4,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

FocusCores of Multilayer Graphs 多层图的FocusCores

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-12 DOI: 10.1109/TKDE.2025.3597995

Run-An Wang;Zhaonian Zou;Dandan Liu;Xudong Liu

{"title":"FocusCores of Multilayer Graphs","authors":"Run-An Wang;Zhaonian Zou;Dandan Liu;Xudong Liu","doi":"10.1109/TKDE.2025.3597995","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3597995","url":null,"abstract":"Mining dense subgraphs on multilayer graphs offers the opportunity for more in-depth discoveries than classical dense subgraph mining on single-layer graphs. However, the existing approaches fail to ensure the denseness of a discovered subgraph on layers of users’ interest and simultaneously gain partial supports on the denseness from other layers. In this paper, we introduce a novel dense subgraph model called <underline>Focus<underline>Core (FoCore for short) for multilayer graphs, which can pay more attention to the layers focused by users. The FoCore decomposition problem, that is, identifying all nonempty FoCores in a multilayer graph, can be addressed by executing the peeling process with respect to all possible configurations of focus and background layers. Using the nice properties of FoCores, we devise an interleaved peeling algorithm and a vertex-centric algorithm toward efficient FoCore decomposition. We further design a novel cache to minimize the average retrieval time for an arbitrary FoCore without the need for full FoCore decomposition, which significantly improves efficiency in large-scale graph mining tasks. As an application, we propose a FoCore-decomposition-based algorithm to approximate the densest subgraph in a multilayer graph with a provable approximation guarantee. The extensive experiments on real-world datasets verify the effectiveness of the FoCore model and the efficiency of the proposed algorithms.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5890-5904"},"PeriodicalIF":10.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Querying Interval Data on Steroids 查询类固醇的间隔数据

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-11 DOI: 10.1109/TKDE.2025.3597399

Panagiotis Bouros;George Christodoulou;Christian Rauch;Artur Titkov;Nikos Mamoulis

{"title":"Querying Interval Data on Steroids","authors":"Panagiotis Bouros;George Christodoulou;Christian Rauch;Artur Titkov;Nikos Mamoulis","doi":"10.1109/TKDE.2025.3597399","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3597399","url":null,"abstract":"A wide range of applications manage interval data with selections and overlap joins being the most fundamental querying operations. Selection queries are typically evaluated using interval indexing. However, the statethe-of-art HINT index and its competitors, are only designed for single query requests while modern systems receive a large number of queries at the same time. In view of this challenge, we study the batch processing of selection queries on HINT. We propose two novel strategies termed level-based and partition-based, which operate in a per-level fashion, i.e., they collect the results for all queries at an index level before moving to the next. The new strategies reduce the cache misses when climbing the index hierarchy, and in particular, partition-based can prevent scanning every index partition more than once. Our experiments on real-world intervals showed that our batch strategies always outperform a baseline which executes queries in a serial fashion, and that partition-based is overall the most efficient one. Motivated by our shared computation techniques for query batches, we also study overlap joins anew across the entire spectrum of different setups, based on the (pre)-existence of interval indexing. For unindexed inputs, we enhance the state-of-the-art optFS join algorithm with effective partitioning proposed for HINT and for indexed inputs, we propose a novel algorithm HINT-join which concurrently scans the input indices, joining partition pairs with optFS. Our tests showed the advantage of HINT-join over indexed nestedloops solutions that employ either B+-trees or probing a single HINT even powered by our partition-based batch processing.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6120-6134"},"PeriodicalIF":10.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Online Outlier Detection in Open Feature Spaces 开放特征空间的在线异常点检测

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-06 DOI: 10.1109/TKDE.2025.3593895

Heng Lian;Yi He;Di Wu;Zhong Chen;Xingquan Zhu;Xindong Wu

{"title":"Online Outlier Detection in Open Feature Spaces","authors":"Heng Lian;Yi He;Di Wu;Zhong Chen;Xingquan Zhu;Xindong Wu","doi":"10.1109/TKDE.2025.3593895","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3593895","url":null,"abstract":"Outlier detection is essential for data compliance, fraud prevention, and strategic decision-making. Finding outliers relies on study of feature space to find anomalous instances. As the feature dimension increases, it will inevitably complicate the process and hinder the models from finding genuine outliers. In this paper, we investigate an ever-more challenging task, online outlier detection (OOD) problem, where data points to be examined for outlier detection are characterized by two dynamic changes: (1) increasing volume instead of a static set; and (2) evolving feature space instead of a known set. Such instance and feature space dynamics impedes traditional OD techniques reliant on geometric data structure for distinguishing outliers. To aid, we propose a new approach coined <italic>Online Outlier Detection in Open Feature Spaces, which circumvents this limitation by learning a latent hypersphere representation, respectively positioning regular and anomalous data points inside and outside its boundary. The crux of our approach tailors a reconstruction loss, allowing each data point to be represented as an <italic>addition of its pertinent feature embeddings. Each of these embeddings is updated non-intrusively, championing both efficient and incremental learning of the latent hypersphere. Extensive experiments on twelve benchmark datasets underscore the robustness and superior performance of our method against seven leading counterparts.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6091-6106"},"PeriodicalIF":10.4,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Latent Semantics and Anchor Graph Multi-Layer Learning for Multi-View Unsupervised Feature Selection 多视图无监督特征选择的潜在语义和锚图多层学习

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-05 DOI: 10.1109/TKDE.2025.3591515

Qi Liu;Suyuan Liu;Xinwang Liu;Jianhua Dai

{"title":"Latent Semantics and Anchor Graph Multi-Layer Learning for Multi-View Unsupervised Feature Selection","authors":"Qi Liu;Suyuan Liu;Xinwang Liu;Jianhua Dai","doi":"10.1109/TKDE.2025.3591515","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3591515","url":null,"abstract":"In recent years, multi-view unsupervised feature selection has gained significant interest for its ability to efficiently handle multi-view datasets while offering better interpretability. However, most existing methods face the following challenges: First, the presence of noisy features in the data significantly impacts the process of learning accurate feature importance. Second, the selected features contain redundant information due to ignored redundancy between them. Third, graph structure learning is performed on all samples, resulting in large computational and space overheads, which is not conducive to expansion to large-scale data. To address these challenges, we propose a multi-view unsupervised feature selection method based on latent semantics and anchor graph learning. Specifically, this method designs a feature-weighted orthogonal regression and subspace learning framework to suppress noise interference in the consensus latent semantics discovery and anchor graph construction process, enhance the robustness of multi-view representation learning and reduce the computation of graph construction. Meanwhile, the proposed method employs explicit redundancy mitigation mechanisms that penalize discriminative weight allocation to highly correlated features. Furthermore, the proposed method unifies feature weighting, consensus latent semantics discovery, and adaptive graph learning within a multi-layer learning framework, enabling comprehensive feature importance evaluation through interactive learning between multiple layers. Finally, an efficient iterative algorithm is designed to solve the proposed model. The superiority of the proposed algorithm is demonstrated by comparing it with seven state-of-the-art algorithms on seven public multi-view datasets.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6032-6045"},"PeriodicalIF":10.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Exact and Efficient Unlearning for Large Language Model-Based Recommendation 基于大型语言模型推荐的精确高效遗忘

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-04 DOI: 10.1109/TKDE.2025.3594687

Zhiyu Hu;Yang Zhang;Minghao Xiao;Wenjie Wang;Fuli Feng;Xiangnan He

{"title":"Exact and Efficient Unlearning for Large Language Model-Based Recommendation","authors":"Zhiyu Hu;Yang Zhang;Minghao Xiao;Wenjie Wang;Fuli Feng;Xiangnan He","doi":"10.1109/TKDE.2025.3594687","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3594687","url":null,"abstract":"Recent years have witnessed the trend of enhancing recommender systems with large language models (LLMs), namely, LLMRec. A common way is to fine-tune the LLMs with the instruction data transformed from user behaviors, stimulating the recommendation ability of LLMs. Similar to traditional recommender systems, integrating user data into LLMs raises privacy concerns. Users desire a tool to erase the impacts of their sensitive data from the trained models. To meet this user demand, LLMRec unlearning becomes pivotal to enable the removal of unusable data (e.g., historical behaviors) from established LLMRec models. However, existing methods mostly focus on partition strategies and approximate unlearning. These methods are not well-suited for the unique characteristics of LLMRec due to computational costs or incomplete removal. In this study, we propose the Adapter Partition and Aggregation (APA) framework for exact and efficient LLMRec unlearning while maintaining recommendation performance. APA achieves this by retraining PEFT adapters using data partitioning, constructing adapters for partitioned training data shards, and retraining only the affected adapters. To preserve recommendation performance and avoid significant inference costs, APA incorporates balanced and heterogeneous data partitioning, and parameter-level adapter aggregation with sample-adaptive adapter attention for each testing sample. Extensive experiments demonstrate the effectiveness and efficiency of our method.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5866-5877"},"PeriodicalIF":10.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Can Uncertainty Quantification Improve Learned Index Benefit Estimation? 不确定性量化能改善学习指标效益估计吗？

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-08-04 DOI: 10.1109/TKDE.2025.3591237

Tao Yu;Zhaonian Zou;Hao Xiong

{"title":"Can Uncertainty Quantification Improve Learned Index Benefit Estimation?","authors":"Tao Yu;Zhaonian Zou;Hao Xiong","doi":"10.1109/TKDE.2025.3591237","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3591237","url":null,"abstract":"Index tuning is crucial for optimizing database performance by selecting optimal indexes based on workload. The key to this process lies in an accurate and efficient benefit estimator. Traditional methods relying on what-if tools often suffer from inefficiency and inaccuracy. In contrast, learning-based models provide a promising alternative but face challenges such as instability, lack of interpretability, and complex management. To overcome these limitations, we adopt a novel approach: quantifying the uncertainty in learning-based models’ results, thereby combining the strengths of both traditional and learning-based methods for reliable index tuning. We propose <sc>Beauty, the first uncertainty-aware framework that enhances learning-based models with uncertainty quantification and uses what-if tools as a complementary mechanism to improve reliability and reduce management complexity. Specifically, we introduce a novel method that combines AutoEncoder and Monte Carlo Dropout to jointly quantify uncertainty, tailored to the characteristics of benefit estimation tasks. In experiments involving sixteen models, our approach outperformed existing uncertainty quantification methods in the majority of cases. We also conducted index tuning tests on six datasets. By applying the <sc>Beauty framework, we eliminated worst-case scenarios and more than tripled the occurrence of best-case scenarios.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5823-5837"},"PeriodicalIF":10.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Adaptive Magnetic-Graph Clustering 自适应磁图聚类

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-31 DOI: 10.1109/TKDE.2025.3594622

Rui Zhang;Yuelong Cheng;Xiang Shi;Xuelong Li

{"title":"Adaptive Magnetic-Graph Clustering","authors":"Rui Zhang;Yuelong Cheng;Xiang Shi;Xuelong Li","doi":"10.1109/TKDE.2025.3594622","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3594622","url":null,"abstract":"Graph representation provides a more effective method for describing the underlying data relationships. Nonetheless, the vast majority of data consists solely of feature information without a corresponding graph structure, rendering graph representation techniques ineffective. Much of the existing research on graph data has concentrated on how to effectively characterize graph nodes, with little focus on how to adaptively construct internal structures and potential connections between the sample pairs. On the other hand, the existing graph construction techniques generate linear inter-instance affinity distributions based on a probabilistic perspective, which might not give a true picture of the relationships. To overcome the above problems, motivated by the fact that sample and inter-sample affinities can be viewed as the source and strength of the magnetic field, respectively, a novel tangent-based affinity measurement algorithm that utilizes a parameter to dynamically adjust the sparsity of the magnetic field is derived. In addition, Adaptive Magnetic-Graph Clustering (AMGC) is designed for graph representation and clustering. AMGC ensures instance-level and cluster-level consistency using a novel dual decoder, where the reconstructed graph retains local affinity and global topology, and contrastive learning defines new sample pairs based on positive-incentive noise, making the learned embedding more discriminative. Eventually, we perform empirical experiments to demonstrate the superiority of the model.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"5755-5766"},"PeriodicalIF":10.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Robust Cross-Platform News Event Detection via Self-Supervised Modality Complementation 基于自监督模态互补的鲁棒跨平台新闻事件检测

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-31 DOI: 10.1109/TKDE.2025.3594200

Zehang Lin;Zhenguo Yang;Qing Li

{"title":"Robust Cross-Platform News Event Detection via Self-Supervised Modality Complementation","authors":"Zehang Lin;Zhenguo Yang;Qing Li","doi":"10.1109/TKDE.2025.3594200","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3594200","url":null,"abstract":"Multimodal news event detection aims to identify and categorize significant events across media platforms using multimodal data. Previous work was limited to a single platform and assumed complete multimodal data. In this paper, we explore a novel task of cross-platform multimodal news event detection to enhance model generalization for cross-platform scenarios. We propose a Self-Supervised Modality Complementation (SSMC) method to tackle the challenges of incomplete modalities and platform heterogeneity presented in this task. Specifically, a Missing Data Complementation (MDC) module is designed to overcome the limitations caused by incomplete modalities. It employs a separation mechanism that distinguishes between modality-specific and modality-shared features across all modalities, allowing for the augmentation of missing modalities with information extracted from common features. Meanwhile, a Multimodal Self-Learning (MSL) module addresses platform heterogeneity by extracting pseudo labels from the target platform’s multimodal views and incorporating a self-penalization mechanism to reduce reliance on low-confidence labels. Additionally, we collect a comprehensive cross-platform news event detection (CNED) dataset encompassing 37,711 multimodal samples from Twitter, Flickr, and online news media, covering 40 public news events verified by Wikipedia. Extensive experiments on the CNED dataset demonstrate the superior performance of our proposed method.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6147-6158"},"PeriodicalIF":10.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

One-Pass Online Learning Under Feature Evolution Data Streams With a Fast Rate 快速特征演化数据流下的一次性在线学习

IF 10.4 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-07-29 DOI: 10.1109/TKDE.2025.3592685

Peng Zhang;Hongpeng Yin;Xuanhong Deng;Sheng-Qing Lv

{"title":"One-Pass Online Learning Under Feature Evolution Data Streams With a Fast Rate","authors":"Peng Zhang;Hongpeng Yin;Xuanhong Deng;Sheng-Qing Lv","doi":"10.1109/TKDE.2025.3592685","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3592685","url":null,"abstract":"Learning under feature evolution data streams has attracted widespread attention in recent years. Existing methods usually assume that the model predicts and learns from all instances in the data stream. However, when the data stream rate is faster than the model update rate, the model can only learn from some instances. Therefore, this assumption may not always hold in practical scenarios. Additionally, existing methods often update based only on the current instance, ignoring the impact of data stream changes, which further limits their application in practical data streams. This paper proposes a novel learning paradigm to solve this problem: Online Learning under Feature Evolution data streams with A Fast Rate, called OLFE-FR. Specifically, OLFE-FR introduces the concept of relative rate to adaptively determine the prediction mode and update node of the model in the data stream. Additionally, OLFE-FR proposes an adaptive learning rate adjustment strategy based on the upper bound of dynamic regret minimization. This strategy enables the model to find a suitable learning rate based on weights change induced by known data stream variations before using the instance update. Theoretical analysis and experimental results show that OLFE-FR can effectively handle feature evolution data streams with a fast rate.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 10","pages":"6075-6090"},"PeriodicalIF":10.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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