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A Privacy-Preserving Cross-Domain recommendation algorithm for Industrial IoT Devices

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 227-237页

作者： Yu, Xu Peng, Qinglong Lv, Hongwu Zhan, Dingjia Hu, Qiang Du, Junwei Gong, Dunwei China Univ Petr Qingdao Inst Software Qingdao 266580 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

recommendation algorithms have been initially applied on the online business platform of industrial Internet of Things (IoT) devices. However, traditional recommendation algorithms are often difficult to solve the data sparsity problem. In fact, online shoppers are often accompanied by consumption behavior of other heterogeneous products, so we combine the consumer behavior of other heterogeneous products in the auxiliary domain to improve the recommendation performance of industrial IoT devices in the target domain. Due to privacy-preserving requirements, the original scoring information of the auxiliary domain is often not allowed to be directly shared with the target domain. Therefore, we propose a Privacy-Preserving Cross-Domain recommendation algorithm for industrial IoT devices. First, the non-privacy preference features are extracted through the auxiliary domain scoring data. Next, the extracted preference features are fused with the target domain information. Extensive experiments have been conducted on the Amazon dataset to verify the effectiveness of our method.

关键词： Decoding Privacy Recommender systems Optimization Industrial Internet of Things Data mining Feature extraction Industry 40 industrial Internet of Things privacy-preserving recommendation algorithm

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miRTRS: A recommendation algorithm for Predicting miRNA Targets

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第3期17卷 1032-1041页

作者： Jiang, Hui Wang, Jianxin Li, Min Lan, Wei Wu, Fang-Xiang Pan, Yi Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Univ South China Sch Comp Sci & Technol Hengyang 421001 Peoples R China Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 5A9 Canada Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.

关键词： Prediction algorithms Bipartite graph Feature extraction Prediction methods Gene expression Software algorithms miRNA target recommendation algorithm bipartite graph

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Research on Singular Value Decomposition recommendation algorithm Based on Data Filling

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023年第3期16卷 1-15页

作者： Liu, Yarong Huang, Feiyang Xie, Xiaolan Huang, Haibin Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin Guangxi Peoples R China Guilin Univ Technol Sch Informat Sci & Engn Guilin Guangxi Peoples R China

In the era of big data, the problem of information overload has become increasingly prominent. recommendation systems are widely studied due to the problem. Due to the sparseness of data, the recommendation effect is not always ideal. To alleviate the problem of data sparsity, a singular value decomposition recommendation algorithm based on data filling is proposed. First, an improved Tanimoto similarity coefficient calculation method is proposed to calculate the similarity, and effective interpolation data is generated for the singular value decomposition model according to the proposed prediction formula. The experimental results show that when using the same dataset MovieLens100K, compared with several commonly used recommendation algorithms, the improved algorithm improves the prediction accuracy of the model, In the best case, RMSE is 10.1% lower than KNNBasic, 7.8% lower than Slope One algorithm, 6.9% lower than SVD algorithm, and 4.8% lower than SVD++ algorithm, verifying that this method can improve the recommendation quality.

关键词： Big Data Data Filling Data Sparse recommendation algorithm Similarity Calculation Singular Value Decomposition

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A Blended Collaborative Teaching Mode in Language Learning Based on recommendation algorithm

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2021年第23期16卷 111-126页

作者： Wang, Ting Zibo Vocat Inst Zibo Peoples R China

With the continuous expansion of economic globalization, English, as the official language in the world, has a high popularity. It can be related to a better development in the future. Meanwhile, with the continuous progress of technology in modern society, multimedia online teaching has become the main means in colleges and universities, which especially brings great changes to foreign language teaching. However, foreign language teaching is facing many difficulties such as large number of contents, less lessons and low participation of students in class. Therefore, the reform of college foreign language teaching is imperative. Based on blended collaborative teaching mode, the paper has designed a "Three Classes" composed of "Communication Class", "Famous Teacher Class" and "Elite Online Class". Teaching process has been designed according to the cognitive process of "Previewing before class, consolidating in class and expanding after class". In addition, recommendation algorithm has been used to collect data on four dimensions, namely, involvement in foreign language learning, content completion of foreign language learning, interaction in foreign language learning and effectiveness of foreign language learning. A performance evaluation system for foreign language learning supported by big data has been established. The results of teaching practice have proved that the blended collaborative teaching mode implemented in the study can be integrated into the whole teaching process through intelligent data analysis, information sharing and technology application, improving teaching quality, enhancing the interaction between teachers and students, which is worthy of promotion and application.

关键词： blended teaching collaborative learning college foreign language recommendation algorithm

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Research on adaptive recommendation algorithm for big data mining based on Hadoop platform

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INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY 2019年第4期12卷 213-220页

作者： Zhang, Jinming Yantai Vocat Coll Dept Informat Technol Yantai 264670 Shandong Peoples R China

Aiming at the problems of low data recall rate, poor data mining accuracy and poor redundant data interference ability of traditional data mining technology, an adaptive recommendation algorithm for big data mining based on Hadoop platform is proposed. In this algorithm, a distributed storage structure model of big data is constructed on Hadoop cloud platform;the statistical regression analysis method is adopted to construct a big data similarity mathematical model;the autocorrelation matching detection method is adopted to extract correlation features of big data and Backlund transform is adopted to decompose time-frequency variation characteristics of the correlation features of big data to design an adaptive recommendation model for big data mining based on Hadoop platform to optimise the performance of big data mining on Hadoop platform. The experimental results show that the accuracy of data mining increases with the number of iterations. Has a strong ability to redundant interference.

关键词： Hadoop platform big data mining recommendation algorithm distributed storage

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User collaborative filtering recommendation algorithm based on adaptive parametric optimisation SSPSO

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2017年第6期8卷 580-592页

作者： Pan, Xiuqin Zhou, Wenmin Lu, Yong Li, Ruixiang Minzu Univ China Sch Informat Engn Beijing Peoples R China

recommendation algorithm is one of the hot issues in the field of computer science, and is widely used in many aspects. Various types of e-commerce systems and applications need to use recommendation system to support. Collaborative filtering recommendation algorithm has been widely used in e-commerce system for its high recommendation accuracy. In order to improve the performance of the process of clustering and selection of nearest neighbours in collaborative filtering, there are several optimisation proposals in this paper directly to the recommendation algorithm which is based on the adaptive parametric optimisation semi-supervised PSO clustering (APO_SSPSO). This paper uses MovieLens data sets to compare the performance of the proposed method and the traditional collaborative filtering recommendation algorithm. Simulation has proved that this recommendation algorithm has accuracy and effectiveness in enhancing the performance of user collaborative filtering recommendation system. And to some extent, the algorithm has minimised space consumption.

关键词： user collaborative filtering recommendation algorithm particle swarm optimisation PSO semi-supervised learning

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Human resource recommendation algorithm based on improved frequent itemset mining

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 126卷 284-288页

作者： Zhaoshan, Liu Yiming, Ma Huihua, Zheng Dege, Liu Jing, Liu Taishan Univ Sch Econ & Management Tai An 271000 Shandong Peoples R China Nueva Ecija Univ Sci & Technol Grad Sch Cabanatuan 3100 Philippines Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China

There are large personalized differences among human resources, and the resource recommendation process easily encounters the problem of data loss, resulting in a short length of the final recommended resource and less information about the actual human resources included. To solve this problem, this research proposes a human resource recommendation algorithm based on improved frequent itemset mining. The dataset containing single items is called, the human resource is mined to improve frequent itemset, the positive matrix factorization (PMF) model is used to process the implicit features in human resource, and the similarity parameters of the human resource dataset are calculated. The algorithm of collaborative filtering is used to divide the recommendation mechanism, and the loss of data generated during the recommendation is controlled, thereby completing the construction of the human resource recommendation algorithm. The hardware and software environment for algorithm operation were constructed. The human resource dataset was arranged. Tests were conducted using the recommendation algorithm based on neural network, recommendation algorithm based on item weight ranking and the proposed recommendation algorithm. The test results showed that the proposed recommendation algorithm recommended the longest length of resources and the most information contained within the resources. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Improved frequent itemset Human resource recommendation algorithm Data loss PMF model Implicit feature

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A collaborative filtering recommendation algorithm based on DeepWalk and self-attention

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2023年第3期26卷 296-304页

作者： Guo, Jiaming Wen, Hong Huang, Weihong Yang, Ce Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China

Graph embedding is one of the vital technologies in solving the problem of information overload in recommendation systems. It can simplify the vector representations of items and accelerates the calculation process. Unfortunately, the recommendation system using graph embedding technology does not consider the deep relationships between items when it learns embedding vectors. In order to solve this problem, we propose a collaborative filtering recommendation algorithm based on DeepWalk and self-attention in this paper. This algorithm can enhance the accuracy in measuring the similarity between items and obtain more accurate embedding vectors. Chronological order and mutual information are used to construct a weighted directed relationship graph. Self-attention and DeepWalk are utilised to generate embedding vectors. Then item-based collaborative filtering is utilised to obtain recommended lists. The result of the relative experiments and evaluations on three public datasets shows that our algorithm is better than the existing ones.

关键词： DeepWalk self-attention mutual information collaborative filtering recommendation algorithm

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Analysis of clothing structure and management in clothing design oriented to market demand via recommendation algorithm

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ELECTRONIC COMMERCE RESEARCH 2023年 1-22页

作者： Hu, Yuli Wuzhou Univ Sch Jewelry & Art Design Wuzhou 543003 Peoples R China

With the development and progress of the times, whether it is the practicality or fashion of clothing, people's requirements for clothing are getting higher and higher. Clothing is an important part of people's daily life. With the improvement of people's overall quality, there are new requirements for the overall style of clothing, such as style, color, fabric comfort, etc. Clothing design has a non-negligible impact on clothing structure and clothing management. In this paper, a collaborative filtering clothing recommendation algorithm based on image visual features is designed. The algorithm uses the matrix decomposition model to obtain the user feature partial favorability matrix and the commodity feature possession matrix through the user-item scoring information. Experiments show that compared with the benchmark algorithm Funk-SVD, the recall, precision, and F1 scores are improved. Therefore, our algorithm can effectively analyze clothing design and clothing structure management, and give better suggestions for people.

关键词： Analysis of clothing structure Management in clothing design Oriented to market demand recommendation algorithm

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Research on K12 Exercise recommendation algorithm based on Educational Neuroscience 22

Research on K12 Exercise Recommendation Algorithm based on E...

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22nd IEEE International Conference on Advanced Learning Technologies (ICALT)

作者： Zhang, Shaowei Li, Zhizhuang Li, Xiaoli Huang, Xiaoke Long Spring Educ Grp Inst Educ Sci Beijing Peoples R China

ISBN: (数字)9781665495196

ISBN: (纸本)9781665495196

In order to help primary and middle school students improve their self-efficacy and reduce their workload, a K12 exercise recommendation algorithm based on educational neuroscience is proposed in this paper. Experimental tests show that, under the same number of classroom exercises, the exercises recommended by the algorithm can help the target students to achieve greater ability improvement, and thus achieve the effect of reducing the workload of primary and secondary school students.

关键词： educational neuroscience Artificial neural network recommendation algorithm Course load Self-efficacy

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