Wei Yu

Wuhan University

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i-10 index

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Document Type

Co-Authors

Shijun Li

Wuhan University

Junpeng Chen

Nanjing University of Finance and Economics

Kah Bin Lim

Universiti Sains Malaysia

Yonggang Ding

Nanjing University of Chinese Medicine

Tiehua Du

Nanyang Polytechnic

Shuai Wang

Lanzhou University

Jianbin Zheng

Wuhan University of Technology

Jiaheng Cao

Central China Normal University

Maosheng Yang

Ningxia University

Mingjie Hu

Hebei University

Cooperative Institutions

Wuhan University

Chinese Academy of Sciences

Jiangnan University

Tsinghua University

University of Chinese Academy of Sciences

Beijing Institute of Technology

Nanyang Technological University

Huazhong University of Science and Technology

Nanjing University of Science and Technology

Shanghai Jiao Tong University

Author Statistics

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Cross-Domain Collaborative Recommendation by Transfer Learning of Heterogeneous Feedbacks

Lecture notes in computer science (2015)

Jun Wang Shijun Li Sha Yang Yonggang Ding Wei Yu

Transfer of learning

Similarity (geometry)

Bridge (graph theory)

10.1007/978-3-319-26187-4_13

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Citations (6)

FOAF-based distributed desktop system for programs recommendation

Wei Yu Shijun Li Yunlu Zhang

Recent research has shown that simple network user recognition is not enough for FOAF (Friend of a Friend) user analysis, and meanwhile more and more researchers focus on user interest analysis and products recommendation. Extremely dispersed mapping information of customer relationship and space storage dispersion make it a challenge to effectively mine and analyze the FOAF information. In this paper, we apply FOAF and DBlink (database links) to recognizing the users' identity and to mining the interest of users so as to recommend latest online TV and movie programs for users from various online programs web sites and whose FOAF flies stored in different databases and different computers. This essay focuses on applying FOAF to a latest online TV program recommendation system for a particular user from various online video web sites he/she has registered in. The article describes the approach to such services based on HMM (Hidden Markov Model). For the protection of user privacy, the system is used as local-service desktop model. We conduct experiments to illustrate users' high degree of satisfaction to our techniques.

User analysis

10.1109/icife.2010.5609277

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Citations (5)

A unified neural model for review-based rating prediction by leveraging multi-criteria ratings and review text

Cluster Computing (2018)

Yonggang Ding Shijun Li Wei Yu Jun Wang Mengjun Liu

Complement

Feature (linguistics)

Representation

10.1007/s10586-018-2098-y

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Citations (7)

Development and Validity Test of Social Attachment Multidimensional Scale

Frontiers in Psychology (2022)

Maosheng Yang Kwanrat Suanpong Athapol Ruangkanjanases Wei Yu Hongyu Xu

Social attachment can explain well the bond between users and social media, but existing research lacks measures of social attachment scales. To this end, this study takes attachment theory as the basis for scale development. On the basis of the development of multidimensional scales for adult, brand, and local attachment, it combines existing relevant studies on social attachment, selects three representative social media such as TikTok, WeChat, and MicroBlog as theoretical samples, explores the concept and structure of social attachment, and develops a social attachment scale through qualitative interviews and open-ended questionnaires. This study applied SPSS 24.0 and Mplus 7.0 to test the social attachment scale. The findings reveal that social attachment consists of three constructs: social connection, social dependence and social identity, and the scale possesses high reliability and validity. This study has developed and validated a social attachment scale in the context of social software use, realizing a quantitative study of social attachment and providing a basis for future empirical research related to social attachment.

Empirical Research

10.3389/fpsyg.2021.757777

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Citations (12)

One Comprehensive Method to Analyze Semiconductor Manufacturing Data by “Piecewise” Regression

2022 China Semiconductor Technology International Conference (CSTIC) (2020)

Lin Gu Wei Yu

Big data analytics is a powerful tool in smart manufacturing (SM). Semiconductor manufacturing data is complex, and also need big data methods to realize correlation analysis. Traditional regression, such as fitting data with certain formula, can't reveal the potential correlation between some semiconductor data. Here, "piecewise" regression (PWR) is proposed, in which raw data is divided into groups, to enhance the potential correlation. This method can be applied to different kinds of semiconductor, such as inline, WAT and CP, to highlight potential correlation and realize predictive analysis.

Semiconductor device fabrication

10.1109/cstic49141.2020.9282439

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Citations (0)

Informal Learning Model Design Based on Micro-Blog

Wei Yu Xing Ruonan Kuang Rongrong

Informal learning attracts more and more attention of people, as a new mobile Internet business, Mblog has some features such as immediate, mobility, personal, convenient and so on. So it is suitable for informal learning. Based on the background of Web2.0, this paper firstly analyzes the feasibility of constructing the informal learning mode by using micro-blog. Then it analyzes design principle of informal learning mode from four aspects: constructing learning team by classification, constructing learning content by collaboration, controlling information transmission systemic, and dominating learning environment. Finally we design Mblog-based informal learning mode.

Informal Learning

Mode (computer interface)

Social Learning

Learning design

10.1109/itme.2015.93

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Citations (0)

MICCF: A Mutual Information Constrained Clustering Framework for Learning Clustering-Oriented Feature Representations

ACM Transactions on Knowledge Discovery from Data (2024)

Hongyu Li Lefei Zhang Kehua Su Wei Yu

Deep clustering is a crucial task in machine learning and data mining that focuses on acquiring feature representations conducive to clustering. Previous research relies on self-supervised representation learning for general feature representations, such features may not be optimally suited for downstream clustering tasks. In this article, we introduce MICCF, a framework designed to bridge this gap and enhance clustering performance. MICCF enhances feature representations by combining mutual information constraints at different levels and employs an auxiliary alignment mutual information module for learning clustering-oriented features. To be specific, we propose a dual mutual information constraints module, incorporating minimal mutual information constraints at the feature level and maximal mutual information constraints at the instance level. This reduction in feature redundancy encourages the neural network to extract more discriminative features, while maximization ensures more unbiased and robust representations. To obtain clustering-oriented representations, the auxiliary alignment mutual information module utilizes pseudo-labels to maximize mutual information through a multi-classifier network, aligning features with the clustering task. The main network and the auxiliary module work in synergy to jointly optimize feature representations that are well-suited for the clustering task. We validate the effectiveness of our method through extensive experiments on six benchmark datasets. The results indicate that our method performs well in most scenarios, particularly on fine-grained datasets, where our approach effectively distinguishes subtle differences between closely related categories. Notably, our approach achieved a remarkable accuracy of 96.4% on the ImageNet-10 dataset, surpassing other comparison methods. The code is available at https://github.com/Li-Hyn/MICCF.git .

Feature (linguistics)

Feature Learning

10.1145/3672402

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Citations (0)

Social Attachment Questionnaire

PsycTESTS Dataset (2022)

Maosheng Yang Kwanrat Suanpong Athapol Ruangkanjanases Wei Yu Hongyu Xu

10.1037/t85489-000

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Citations (0)

Build Yourself Before Collaboration: Vertical Federated Learning with Limited Aligned Samples