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recommendation algorithm Integrating CNN and Attention System in Data Extraction

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Computers, Materials & Continua 2023年第5期75卷 4047-4063页

作者： Yang Li Fei Yin Xianghui Hui College of Information and Management Science Henan Agricultural UniversityZhengzhou450046China

With the rapid development of the Internet globally since the 21st century,the amount of data information has increased *** helps improve people’s livelihood and working conditions,as well as learning ***,data extraction,analysis,and processing have become a hot issue for people from all walks of *** recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low *** solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network *** the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ***,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes *** recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction *** obtains data name features through a convolution ***,the top pooling layer obtains the length *** attention system layer obtains the characteristics of the data *** results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present *** proposed algorithm shows excellent accuracy and robustness.

关键词： Data extraction recommendation algorithm CNN algorithm attention model

Short Video recommendation algorithm Incorporating Temporal Contextual Information and User Context

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Computer Modeling in Engineering & Sciences 2023年第4期135卷 239-258页

作者： Weihua Liu Haoyang Wan Boyuan Yan Network Security and Information Management Center Jining UniversityJining272000China School of Computer Science Qufu Normal UniversityRizhao276800China

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software *** short-form mobile video in fragmented time has become the mainstream of user’s ***,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their ***,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand *** the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform *** this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user *** the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short *** remains one more *** also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of *** last,a diverse approach is used to generate a Top-K recommendation list for *** through a case study,we illustrate the accuracy and diversity of the proposed method.

关键词： recommendation algorithm user contexts short video temporal contextual information

recommendation algorithm based on item quality and user rating preferences

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Frontiers of Computer Science 2014年第2期8卷 289-297页

作者： Yuan GUAN Shimin CAI Mingsheng SHANG Web Sciences Center School of Computer Science and EngineeringUniversity of Electronic Science and Technology of China Chengdu 611731 China

Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filter- ing CF family, have problems ness. These problems hinder such as scalability and sparse- further developments of rec- ommender systems. We propose a new recommendation al- gorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accu- racy in rating prediction compared with the traditional ap- proaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.

关键词： recommendation algorithm item quality userrating preferences RMSE

Design and Implementation of Collaborative Filtering recommendation algorithm for Multi-layer Networks

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国际计算机前沿大会会议论文集 2021年第1期 32-50页

作者： Ling Gou Lin Zhou Yuzhi Xiao Computer Department Qinghai Normal UniversityXining 810016QinghaiChina Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining 810008QinghaiChina Key Laboratory of Tibetan Information Processing Ministry of EducationXining 810008QinghaiChina

With the continuous development of mobile communications and Internet technologies,the marketing model of the communications industry has shifted from calling-based to social APP-based personalized *** order to improve the accuracy of recommendation,this paper proposes a recommendation algorithm for social *** data was firstly used to construct a“user-APP”two-layer communication network model,and then the traditional collaborative filtering recommendation technology was integrated to reconstruct similar users and similar APP network *** bipartite graph weight distribution method was taken to recommend targets in the obtained network *** experimental simulation shows that,in view of the characteristics of the twolayer communication network,compared with the traditional recommendation algorithm,the algorithm effectively improves the accuracy of the score prediction.

关键词： Two-layer communication network Social network analysis recommendation algorithm Collaborative filtering algorithm

Design of Hybrid recommendation algorithm in Online Shopping System

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Journal of New Media 2021年第4期3卷 119-128页

作者： Yingchao Wang Yuanhao Zhu Zongtian Zhang Huihuang Liu Peng Guo Hunan University of Finance and Economics ChangshaChina University Malaysia Sabah Kota KinabaluMalaysia

In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to ***,the accuracy of the recommendation algorithm is a primary *** far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation ***-based recommendation algorithms and collaborative filtering algorithms have their own *** content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and *** the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better *** article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content *** similarity gets the recommendation list.

关键词： recommendation algorithm hybrid recommendation algorithm content-based recommendation algorithm collaborative filtering algorithm

A Comparative Study of the recommendation algorithm Applicate in the Network of Educational Resources Platform

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A Comparative Study of the Recommendation Algorithm Applicat...

Improved Collaborative Filtering recommendation algorithm Based on Weibo Content

2013教育技术与信息系统国际会议

作者： Han Jun Yang Fan Liu Jing Tong Jun Chen Shuo Department of Educational Technology Capital Normal University

Recommended information that meet their interests and needs for users. With the increasing number of educational resources, recommendation system has been introduced in construction of educational resources platform. This paper is to analyze the status of educational resources and educational resource platform. According to the characteristics of the different recommendation algorithms compared advantages and disadvantages of recommendation algorithms in the application of educational resources platform, and analyze the causes of problems and consequences.

关键词： Educational Resources The Network Education Resources Educational Resources Platform recommendation algorithm

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Improved Collaborative Filtering Recommendation Algorithm Ba...

High-end Equipment Requirement Generation Model Based on recommendation algorithm

第三十九届中国控制会议

作者： Juntao Xue Ruohan Ma Yunfeng Zhao Junjie Hei School of Electrical Automation and Information Engineering Tianjin University

Taking Weibo content as the research object and aiming at the disadvantages of only popular recommendation for Weibo, we propose personalized recommendation scheme. The improved user-based collaborative filtering algorithm is used to give a recommendation list. In this paper, the keywords extraction technology is used to extract the keywords of Weibo content as the project, and the results of the sentiment analysis algorithm are used as the scores to form the project score matrix. Then,the similarity of the item and the score are calculated by the cosine similarity and the Jaccard coefficient, respectively, the final similarity result is obtained by weighted fusion. The experimental results show that the improved algorithm proposed in this paper can effectively solve the problem that the microblog content has no scoring system, which helps to improve the performance of the recommendation system and obtain better recommendation results.

关键词： Weibo recommendation algorithm Similarity Calculation

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High-end Equipment Requirement Generation Model Based on Rec...

Research on recommendation algorithms Based on Cloud Models in Probabilistic Linguistic Environments

第36届中国控制会议

作者： Yuren Wang Zhiwei Yang Yanqing Ye Yajie Dou College of Information System and Management National University of Defense Technology

ISBN: (纸本)9781538629185

High-end equipment manufacturing plays a core role in the equipment manufacturing *** user requirements is crucial in the process of high-end equipment manufacturing.A requirement generation model for the high-end equipment manufacturing is proposed based on recommendation algorithm,which can recommend some requirements to assist users in validating and managing their ***,the idea of using a recommendation algorithm to analyze the user requirement is ***,an advanced collaborative filtering algorithm is illustrated,towards capturing the characteristics of the high-end equipment as well as addressing the shortcomings of traditional ***,by applying the algorithm,a requirement generation model is established,which can recommend a personalized design of the configuration for users according to their historical behavior *** users can choose from the designs to validate and change their ***,the proposed recommendation model is evaluated by an example of individualized aircraft manufacturing,which verifies its feasibility.

关键词： requirement generation high-end equipment recommendation algorithm design of configuration

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Journal of Systems Science and Information 2024年第1期12卷 96-112页

作者： Peng YANG Xifeng MA Meng WEI Chunsheng CUI Libin CHE College of Computer and Information Engineering Henan University of Economics and LawZhengzhou 450046China

To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language ***,this paper quantifies the attributes in the review text based on the probabilistic linguistic term ***,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud ***,the products are recommended and sorted based on the digital characteristic value of the cloud *** algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.

关键词： recommendation algorithm cloud model probabilistic linguistic term set text reviews