检索结果-内蒙古大学图书馆

Representation learning: serial-autoencoder for personalized recommendation

Frontiers of Computer Science 2024年第4期18卷 61-72页

作者： Yi ZHU Yishuai GENG Yun LI Jipeng QIANG Xindong WU School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of the People’s Republic of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关键词： personalized recommendation autoencoder representation learning collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Efficient Algorithms for Personalized PageRank Computation: A Survey

引用

IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2024年第9期36卷 4582-4602页

作者： Yang, Mingji Wang, Hanzhi Wei, Zhewei Wang, Sibo Wen, Ji-Rong Renmin Univ China Beijing 100872 Peoples R China Pazhou Lab Huangpu Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Methods MOE Key Lab Data Engn & Knowledge Engn Guangzhou 510555 Guangdong Peoples R China Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes s and t, the PPR value pi(s)(t)equals the probability that an alpha-discounted random walk from s terminates at t and reflects the importance between s and tin a bidirectional way. As a generalization of Google's celebrated PageRank centrality, PPR has been extensively studied and has found multifaceted applications in many fields, such as network analysis, graph mining, and graph machine learning. Despite numerous studies devoted to PPR over the decades, efficient computation of PPR remains a challenging problem, and there is a dearth of systematic summaries and comparisons of existing algorithms. In this paper, we recap several frequently used techniques for PPR computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective. We classify these approaches based on the types of queries they address and review their methodologies and contributions. We also discuss some representative algorithms for computing PPR on dynamic graphs and in parallel or distributed environments.

关键词： Vectors Surveys Clustering algorithms Heuristic algorithms Classification algorithms Reviews Measurement uncertainty Graphs and networks graph algorithms PageRank personalized PageRank

来源：评论

学校读者我要写书评

暂无评论

Robust sparse orthogonal basis clustering for unsupervised feature selection

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Miao, Jianyu Zhao, Jingjing Yang, Tiejun Tian, Yingjie Shi, Yong Xu, Mingliang Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China

Unsupervised Feature Selection (UFS), which identifies the optimal-related feature subset from the original feature set to lower the dimensionality of data without label information, has had a high profile in recent years. Given the absence of label information, the existing UFS approaches usually utilize graph and manifold learning techniques to retain the intrinsic structure of the data. The inclusion of irrelevant and redundant features and noise, would inevitably lower the quality of the structure. For this purpose, in this paper, we come up with Robust Sparse Orthogonal Basis Clustering (RSOBC), a novel method for UFS that integrates feature selection process with clustering task into a unified framework. Instead of explicitly utilizing the pre-computed local information, such a strategy focuses on exploring the inherent clustering structures of data. RSOBC leverages the log-based function as the loss to lessen the effect of noise and outliers, thereby enhancing its robustness. To select the more useful and discriminative features, the l2,1 norm is employed as the sparse regularization to encourage sparsity of the projection matrix. Meanwhile, we adopt the low redundancy regularization to make the weights of the correlated features small. In this way, the correlated features cannot be selected simultaneously. Consequently, the projection matrix, centroid matrix and cluster label matrix are learned simultaneously, such that the intrinsic structure is constructed in amore accurate way. The resulting optimization can be readily tackled by multi-block Alternating Direction Method of Multipliers (ADMM) based algorithm. Comprehensive experiments have been carried out on nine diverse real-world datasets. The results demonstrate that RSOBC surpasses many state-of-the-art UFS approaches, which indicates its effectiveness and superiority.

关键词： Unsupervised feature selection Robust loss function Sparse regularization Low redundancy

来源：评论

学校读者我要写书评

暂无评论

Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

引用

Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 91-106页

作者： XU Wangwang XU Liangfeng LIU Ninghui LU Na Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230601China School of Computer and Information Hefei University of TechnologyHefei230601China First Affiliated Hospital of Anhui Medical University Hefei230022China

Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.

关键词： breast cancer deep learning attentional mechanism classification diagnosis

来源：评论

学校读者我要写书评

暂无评论

Comparative research on multi-source heterogeneous data fusion technologies 11

Comparative research on multi-source heterogeneous data fusi...

引用

11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Long, Wen Gao, Jing Guo, Man School of Economics and Management University of Chinese Academy of Sciences Beijing100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing100190 China

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect. © 2024 The Authors.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

XAI-driven assessment of urban circular carbon economy: Using China's pilot cities as a case study

引用

URBAN CLIMATE 2025年 61卷

作者： Wang, Ning Zhai, Hao Bai, Yubing Fan, Yu Beijing Informat Sci & Technol Univ Beijing 102206 Peoples R China Beijing Key Lab Green Dev Decis Based Big Data Beijing 102206 Peoples R China Beijing Knowledge Management Res Ctr Beijing 100192 Peoples R China

The confluence of circular economy and carbon emission mitigation strategies has catalyzed the emergence of a concept called the Circular Carbon Economy (CCE). This study introduces a novel CCE evaluation framework targeting urban areas and employs Explainable Artificial Intelligence (XAI) to bolster transparency and interpretability. Through the application of big data mining techniques, key indicators were extracted from literature and policy documents to construct a multi-dimensional CCE evaluation system. This system was integrated with the Explainable Boosting Machine (EBM) model-a key XAI technique-to resolve the precision-interpretability trade-off prevalent in traditional models. The EBM-based framework achieves black-box-level accuracy while providing transparent decision pathways through feature importance quantification. Empirical validation with China's Circular Economy pilot cities demonstrates its applicability, offering a transferable paradigm for global urban CCE assessments. Our findings show that industrial solid waste utilization and the promotion of green buildings are the most critical drivers for advancing CCE in urban areas. The city rankings generated by the EBM model bolster the scientific validity and reliability of our framework. Moreover, the study uncovers significant regional disparities, with cities located in eastern and central China generally exhibiting higher levels of CCE development compared to their western and northeastern counterparts. Based on these insights, we propose three key recommendations-targeted development strategies, differentiated evaluation frameworks, and balanced regional development-to lay the theoretical groundwork and offer practical guidance for urban-level CCE development.

关键词： Circular carbon economy (CCE) Explainable artificial intelligence (XAI) CCE evaluation framework

来源：评论

学校读者我要写书评

暂无评论

Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation 38

Bridging The Gap between Low-rank and Orthogonal Adaptation ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Shen Liu, Haotian Xu, Hongteng Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China

While following different technical routes, both low-rank and orthogonal adaptation techniques can efficiently adapt large-scale pre-training models in specific tasks or domains based on a small piece of trainable parameters. In this study, we bridge the gap between these two techniques, proposing a simple but effective adaptation method based on Householder reflections. Given a pre-trained model, our method fine-tunes its layers by multiplying each frozen weight matrix with an orthogonal matrix constructed by a chain of learnable Householder reflections (HRs). This HR-based orthogonal fine-tuning is equivalent to an adaptive low-rank adaptation. Moreover, we show that the orthogonality of the reflection planes corresponding to the HRs impacts the model capacity and regularity. The analysis motivates us to regularize the orthogonality of the HRs, leading to different implementations of the proposed Householder reflection adaptation (HRA) method. Compared with state-of-the-art methods, HRA achieves superior performance with fewer learnable parameters when adapting large language models and conditional image generators. The code of the experiments is available at https://***/DaShenZi721/HRA, and the method has been merged into the PEFT package. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Cluster Feature Selection Based on Isometric Mapping

引用

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

来源：评论

学校读者我要写书评

暂无评论

Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students 9th

Ensemble Learning Based Employment Recommendation Under Inte...

引用

19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Haiping Zhao, Yifei Wu, Yuchen Chen, Yan Li, Wenhao Zheng, Qinghua Tian, Feng Department of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9783031466632

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Face micro-expression recognition algorithm based on ResNet depth model 8

Face micro-expression recognition algorithm based on ResNet ...

引用

8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Wang, Liquan Zhan, Shu Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education School of Computer and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted;SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASME, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：