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THE APPLICATION OF IPFS SYSTEM INTEGRATING BLOCKCHAIN TECHNOLOGY IN SMART LOGISTICS

MECHATRONIC SYSTEMS AND CONTROL 2024年第4期52卷 284-292页

作者： Chen, Jianxin Jiangsu Vocat Coll Business Sch Digital Commerce & Intelligent Logist Nantong 226011 Peoples R China

A data transmission technology combining blockchain technology and interstellar file system is proposed to address the issue of lack of effective data confidentiality and transmission methods in logistics system data transmission. This technology converts all key operations into transactions on the blockchain, ensuring the recording and verification of the entire operation, and improving the transparency and data security of the logistics process. At the same time, collaborative filtering algorithms and light gradient boosting machine algorithms based on decision tree algorithms are used as recommendation systems to automatically match suitable drivers for order transportation, to optimise order allocation and loading work. The results show that the smart logistics (SL) system designed in the study has a high throughput and a low utilisation rate of system memory. When the concurrency is 800, the throughput of the registration and review function is 701.5/s. Through the integration of blockchain technology and interstellar file systems, as well as the use of collaborative filtering algorithms and decision tree algorithms in conjunction with gradient enhancement framework algorithms, logistics processes have been successfully optimised, improving data security and operational efficiency.

关键词： Blockchain technology IPFS smart logistics collaborative filtering algorithm lightGBM

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Cultural tourism attraction recommendation model based on optimized weighted association rule algorithm

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SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Jiang, Rui Dai, Bin Jiangsu Maritime Inst Sch Humanity & Arts Nanjing 211170 Peoples R China Nanjing Teaching & Res Div Vocat Adult Educ Acad Affairs Management Dept Nanjing 210018 Peoples R China

To address the challenge of users selecting rich tourism resources, this study proposes a model for cultural tourism attraction recommendation using an optimized weighted association rule algorithm. This model includes time and season weight for tourist attraction recommendations. This model proposes improvement methods to address some inherent issues in traditional tourism recommendation models. Firstly, it constructed a recommendation model for cultural tourism attractions, and then optimized the weighted association rule algorithm by incorporating dynamic time weights. It takes into account the user's intended time in the recommendation outcome. Moreover, it incorporated seasonal weights to optimize the weighted rule algorithm for factors such as user travel time and the attractions' peak season during the recommendation process. The experiment indicates that the F1 value of the improved algorithm model proposed in this study reaches 0.952, the accuracy reaches 0.985, the area under the curve reaches 0.955, the Recall value reaches 0.812, and the fitting degree reaches 0.971. The results suggest that the proposed cultural tourism attraction recommendation model, based on an optimized weighted association algorithm, performs well in recommending tourist destinations. This model can have a positive impact on the development of the tourism industry.

关键词： Tourism resource recommendation collaborative filtering algorithm Weighted association rule algorithm Dynamic time weighting Seasonal weight

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Study on personalised recommendation method of online education resources under the background of teaching reform

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2024年第4期34卷 416页

作者： Zheng, Hanyu Changchun Univ Architecture & Civil Engn Coll Cultural & Creat Ind Changchun 130000 Peoples R China

In order to achieve the research goal of solving the problems of accuracy, recall and low F1 value of traditional online education resources personalised recommendation methods, a new personalised recommendation method of online education resources under the background of teaching reform was designed. Using the BERT model to extract learner preference vectors and feature vectors of online educational resources, an improved discrete differential evolution algorithm is designed, which is used to recall and sort online educational resource sequences. Combined with the collaborative filtering algorithm to generate recommendation sequences, personalised recommendation results of online education resources are obtained. Simulation experiments show that the accuracy rate curve of this paper method relatively flat, accuracy rate is always above 93%, the maximum recall rate is 98%, and the F1 mean is 9.67, the recommended results are reliable.

关键词： teaching reform online educational resources personalised recommendation BERT model discrete differential evolution algorithm collaborative filtering algorithm

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Big Data Multi-Strategy Predator algorithm for Passenger Flow Prediction

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 800-810页

作者： Guo, Peng Zhengzhou Univ Ind Technol Sch Management Zhengzhou 450064 Peoples R China

Faced with the rapidly recovering tourism market, accurate prediction of passenger flow can help local authorities achieve more effective resource regulation. Therefore, based on big data technology, a multi-strategy predator algorithm is proposed, which uses the Marine Predator algorithm, combined with regularized extreme learning machines and collaborative filtering algorithms, to achieve accurate passenger flow prediction. The experiment findings denote that the performance parameters of the algorithm are excellent, with extremely strong convergence performance, and only 30 iterations are needed to reach the optimal solution. The fitting degree of this algorithm is 97.8%, which is 6.27% -19.31% higher than that of long and short-term memory networks, random forest algorithms, and support vector machine regression. In actual passenger flow prediction, the error rate of this algorithm is only 2.29%, which is 3.47% - 6.50% higher than the three comparison algorithms. This study provides a new and efficient prediction method for passenger flow prediction. Its excellent predictive performance can not only help relevant departments predict and manage passenger traffic more accurately, but also provide reference for traffic prediction in other fields. Overall, this study has important reference value and practical significance for the research and practice of passenger flow prediction.

关键词： Passemger Flow Prediction regularized extreme learning machine collaborative filtering algorithm Marine Predator algorithm

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DRLAttack: A Deep Reinforcement Learning-Based Framework for Data Poisoning Attack on collaborative filtering algorithms

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Applied Sciences 2025年第10期15卷 5461-5461页

作者： Jiaxin Fan Mohan Li Yanbin Sun Peng Chen Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China

collaborative filtering, as a widely used recommendation method, is widely applied but susceptible to data poisoning attacks, where malicious actors inject synthetic user interaction data to manipulate recommendation results and secure illicit benefits. Traditional poisoning attack methods require in-depth understanding of the recommendation system. However, they fail to address its dynamic nature and algorithmic complexity, thereby hindering effective breaches of the system’s defensive mechanisms. In this paper, we propose DRLAttack, a deep reinforcement learning-based framework for data poisoning attacks. DRLAttack can launch both white-box and black-box data poisoning attacks. In the white-box setting, DRLAttack dynamically tailors attack strategies to recommendation context changes, generating more potent and stealthy fake user interactions for the precise targeting of data poisoning. Furthermore, we extend DRLAttack to black-box settings. By introducing spy users to simulate the behavior of active and inactive users into the training dataset, we indirectly obtain the promotion status of target items and adjust the attack strategy in response. Experimental results on real-world recommendation system datasets demonstrate that DRLAttack can effectively manipulate recommendation results.

关键词： recommendation system collaborative filtering algorithm deep reinforcement learning data poisoning attack

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Examining collaborative filtering algorithms for clothing recommendation in e-commerce

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TEXTILE RESEARCH JOURNAL 2019年第14期89卷 2821-2835页

作者： Hu, Zhi-Hua Li, Xiang Wei, Chen Zhou, Hong-Lei Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China Donghua Univ Fash & Design Coll Shanghai Peoples R China

With the boom in online clothing e-commerce, various web portals and mobile applications apply recommendation methods to improve the sales and consumer satisfaction based on the massive historical records in the big data era. This study examined the collaborative filtering algorithms embedded in the typical recommendation methods for online clothing. The test dataset is constructed with a real-world large-scale instance from one of the largest business-to-consumer e-commerce platforms () in China. Considering the purchasing times and the inverse user frequency, three similarity measures are developed for the cosine-based similarity algorithm. Various numerical experiments are conducted to analyze the recommendation methods and evaluate their performances by three criteria, namely Precision, Recall and Diversity. Because the test instance is large scale, the consumer-goods co-occurrence matrix is reduced to improve the computational performance considering most similar consumers. Using this real-world instance, the algorithms are investigated under the evaluation criteria. The experimental results reveal that the recommendation based on user frequency similarity is very much suitable for the online clothing recommendation;the co-occurrence matrix reduction method is effective to improve the recommendation performance. To verify the proposed methods, the MovieLens dataset is used for comparison. The results show that the proposed method is suitable for sparse co-occurrence matrices. In addition, limitations, managerial implications and future research directions are also discussed.

关键词： collaborative filtering algorithm recommendation system fashion business big data e-commerce

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Environmental protection evaluation of new inorganic non-metallic building materials based on principal component analysis

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2023年第2期67卷 178-193页

作者： Zheng, Lina Zheng, Zhanzhan Chen, Yanmin Qi, Aina Shangqiu Inst Technol Sch Civil Engn Shangqiu 476000 Peoples R China Shangqiu Normal Univ Sch Civil Engn Shangqiu 476000 Peoples R China

In order to improve the accuracy and efficiency of the environmental protection evaluation results of building materials, a new environmental protection evaluation method of inorganic non-metallic building materials based on principal component analysis was proposed. Firstly, knowledge atlas and collaborative filtering algorithm are used to mine evaluation indicators. Then, the evaluation index system is constructed, and the judgment matrix is established to calculate and sort the weight of the evaluation index. Finally, the principal component analysis method is introduced to evaluate the environmental protection performance, and the original evaluation index is standardised to obtain the comprehensive score of the environmental protection performance of building materials, to realise the environmental protection performance evaluation. The experimental results show that the evaluation accuracy of the proposed method is high and the time consumption is short, indicating that the evaluation results are reliable.

关键词： principal component analysis standardised treatment judgment matrix knowledge map collaborative filtering algorithm

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Design and simulation of precision marketing recommendation system based on the NSSVD plus plus algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第13期37卷 8139-8152页

作者： Liu, Yishu Zhang, Wenjian Dongguan City Univ Business Sch Dongguan 523419 Guangdong Peoples R China Nanchang Normal Coll Appl Technol Sch Econ & Management Nanchang 330108 Jiangxi Peoples R China

This article is dedicated to discussing the design and improvement of precision marketing push algorithm under the deep learning target detection system. Scientific progress has made the network form a huge database, and each software platform can accurately locate the user's market by analyzing the data, determine their interests, and carry out marketing pushes. Based on deep learning algorithms and models, simulation training can be performed on user rating data. It clusters users according to their preferences and performs data filtering to control the impact of data sparseness or differences between users. It uses the relevant similarity of users and uses neighborhood collaborative filtering to make accurate judgments and push. This article is based on the SVD++ (Singular Value Decomposition, SVD++) algorithm and optimized to achieve higher push accuracy. The previous explained the SDD (Single Shot MultiBox Detector, SDD) algorithm, Pearson correlation similarity, neighborhood-based collaborative filtering model, neural network model, and Rayleigh channel system to explain the application of the deep learning target detection system in precision marketing, then verify the feasibility of the improved SVD++ algorithm through experiments. In the experiment, through the comparative analysis of UserCF (collaborative filtering, UserCF), Slope one, SVD++, OrdRec, Pure, AllRank, JSVD++, MSSVD++ (Medical Society for the Study of Venereal Diseases, MSSVD++), NSSVD++ (Neighborhood Sampling Singular Value Decomposition) algorithms, the test focuses on JSVD++, MSSVD++, NSSVD++ algorithms. And it is concluded that the NSSVD++ algorithm, that is, the neighborhood sampling method, is the most effective and has the best marketing recommendation effect. Among them, the 1-Call algorithm is 41.62% higher than the SVD++ algorithm, and more than 16.88% higher than other benchmark algorithms, the COV (Covariance, COV) algorithm is improved by more than 12.97%, the CIL algorithm is imp

关键词： Deep learning Precision marketing push Push algorithm Neural network model Neighborhood sampling algorithm collaborative filtering algorithm

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Improved collaborative filtering algorithm In The Research And Application Of Personalized Movie Recommendations

Improved Collaborative Filtering Algorithm In The Research A...

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4th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Xiao Peng Shao Liangshan Li Xiuran Liaoning Tech Univ Huludao 125105 Liaoning Peoples R China Heilongjiang Univ Sci & Technol Haerbin Heilongjiang 150000 Peoples R China

ISBN: (纸本)9781479927913

With the development of the Internet and e-commerce, recommendation system has been widely used. In this paper, the electronic commerce recommendation system, has a further study and focuses on the collaborative filtering algorithm in the application of personalized movie recommendation system. According to the characteristics of movie recommendation system itself and traditional collaborative filtering algorithm of sparse user ratings matrix, this paper is proposed based on a hybrid user-based and item-based collaborative filtering algorithms, and applied to the MovieLens dataset, achieved good effect.

关键词： Recommendation system collaborative filtering algorithm Recommendation algorithm Movie

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The construction of national fitness online platform system under mobile internet technology

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第1期14卷 98-109页

作者： Liang, Xiaojun Kuang, Xin Xu, Yi Xiao, Haibin Zhaoqing Med Coll Minist Publ Fdn Zhaoqing 526020 Guangdong Peoples R China Univ Perpetual Help Syst DALTA Grad Sch Manila 1740 Philippines Guang Dong AIB Polytech Sch Management Guangzhou 510507 Guangdong Peoples R China Guangdong Ecoengn Polytech Minist Basic Educ Guangzhou 510520 Peoples R China Wonkwang Univ Dept Comp & Software Engn Iksan 54538 South Korea

With the rapid development of the mobile Internet, people's demand for information is increasing, and the traditional fitness model is unable to meet the development needs of society. In this study, mobile Internet technology is used to build a new type of green intelligent fitness system. The system can collect users' fitness data and upload the data to the cloud server. And users can obtain their exercise data and their ranks at any time through mobile APP to realize data sharing. At the same time, WebSocket technology is used to realize real-time updates of data, and a collaborative filtering recommendation algorithm is used to analyze users' rating data and recommend intelligent fitness equipment for users. It is found that the system constructed in this study uses the computing power of multiple nodes in the cluster to analyze the fitness data on the cluster rapidly. Based on the collaborative filtering algorithm, the analysis of users is realized, and the recommendation accuracy is up to 89%. This study first puts forward the combination of mobile Internet and traditional fitness industry, which provides a reliable way to promote the development of national fitness.

关键词： Mobile Internet National fitness collaborative filtering algorithm Green fitness User's rating

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