In order to improve the consistency between the recommended retrieval results and user needs,improve the recommendation efficiency,and reduce the average absolute deviation of resource retrieval,a design method of int...In order to improve the consistency between the recommended retrieval results and user needs,improve the recommendation efficiency,and reduce the average absolute deviation of resource retrieval,a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed.The TG-LDA(Tag-granularity LDA)model is proposed on the basis of the standard LDA(Linear Discriminant Analysis)model.The model is used to mine archive resource topics.The Pearson correlation coefficient is used to measure the relevance between topics.Based on the measurement results,the FastText deep learning model is used to achieve archive resource classification.According to the classification results,TF-IDF(term frequency–inverse document frequency)algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval,and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources.The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’needs,can provide users with personalized digital archive resources,and the average absolute deviation of resource retrieval is low,the recommendation efficiency is high,and the utilization effect of archive resources is effectively improved.展开更多
The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit law...The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.展开更多
In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is th...In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is that the consumer’s information demand ascan be thought of consumer’s information demand is leading to trigger the power of consumer network information search behavior, whenconsumer is willing to buy goods, in a certain task under the infl uence of factors, environmental factors, individual factors, consumers and thetask object interaction to form the demand of consumer cognition. Under this basis, this paper proposes the new idea on the related issues thatwill solve the related challenges.展开更多
An integrated implementation framework of an intelligent recommendation system for outdoor video advertising is proposed, which is based on the analysis of audiences' characteristics. Firstly, the images of the scene...An integrated implementation framework of an intelligent recommendation system for outdoor video advertising is proposed, which is based on the analysis of audiences' characteristics. Firstly, the images of the scene and the people who view the video advertisements are captured by the net- work camera deployed on the video advertising terminal side. Then audiences' characteristics can be obtained by applying computer vision technologies : face detection, face tracking, gender recogni- tion and age estimation. Finally, an intelligent recommendation algorithm is designed to decide the most fitting video ads for each terminal according to multi-dimensional statistical information of its reover, a novel face detection method and a new face tracking method have been proposed to meet the practical requirements of the system, of which the average Fl-score is O. 988 and 0. 951 respec- tively.展开更多
With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an importan...With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.展开更多
文摘In order to improve the consistency between the recommended retrieval results and user needs,improve the recommendation efficiency,and reduce the average absolute deviation of resource retrieval,a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed.The TG-LDA(Tag-granularity LDA)model is proposed on the basis of the standard LDA(Linear Discriminant Analysis)model.The model is used to mine archive resource topics.The Pearson correlation coefficient is used to measure the relevance between topics.Based on the measurement results,the FastText deep learning model is used to achieve archive resource classification.According to the classification results,TF-IDF(term frequency–inverse document frequency)algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval,and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources.The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’needs,can provide users with personalized digital archive resources,and the average absolute deviation of resource retrieval is low,the recommendation efficiency is high,and the utilization effect of archive resources is effectively improved.
基金supported by the Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project(No.2021ZD0113604)China Agriculture Research System of MOF and MARA(No.CARS-23-D07)。
文摘The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.
文摘In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is that the consumer’s information demand ascan be thought of consumer’s information demand is leading to trigger the power of consumer network information search behavior, whenconsumer is willing to buy goods, in a certain task under the infl uence of factors, environmental factors, individual factors, consumers and thetask object interaction to form the demand of consumer cognition. Under this basis, this paper proposes the new idea on the related issues thatwill solve the related challenges.
基金Supported by the National High Technology Research and Development Program of China(No.2011AA01A102)the Important Science&Technology Project of Hainan Province(No.JDJS2013006,ZDXM2015103)the Young Talent Frontier Project of Institute of Acoustics,Chinese Academy of Sciences
文摘An integrated implementation framework of an intelligent recommendation system for outdoor video advertising is proposed, which is based on the analysis of audiences' characteristics. Firstly, the images of the scene and the people who view the video advertisements are captured by the net- work camera deployed on the video advertising terminal side. Then audiences' characteristics can be obtained by applying computer vision technologies : face detection, face tracking, gender recogni- tion and age estimation. Finally, an intelligent recommendation algorithm is designed to decide the most fitting video ads for each terminal according to multi-dimensional statistical information of its reover, a novel face detection method and a new face tracking method have been proposed to meet the practical requirements of the system, of which the average Fl-score is O. 988 and 0. 951 respec- tively.
基金This research work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan ProvinceHunan Provincial Key Laboratory of Big Data Science and Technology,Finance and Economics+3 种基金Key Laboratory of Information Technology and Security,Hunan Provincial Higher Education.This research is funded by the Open Foundation for the University Innovation Platform in the Hunan Province,grant number 18K103Open project,Grant Numbers 20181901CRP03,20181901CRP04,20181901CRP05Hunan Provincial Education Science 13th Five-Year Plan(Grant No.XJK016BXX001)Social Science Foundation of Hunan Province(Grant No.17YBA049).
文摘With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.