Design of Library Mobile User Behavior Analysis model for Personalized Information Service

Due to its portability, mobile terminals (mobile phones and similar devices) have become an transfer of information, between people as well as an important tool for network access. Based on user behavior analysis, using the information of data warehouse will be a reasonable quantification of qualitative indicators, draw the user a variety of potential semantic behavior, and user clustering and dimension reduction, the establishment of a recommendation based on user behavior analysis model . This paper based on user behavior analysis, first extract the user factors into the model are data on these factors reduce the dimensions of the conclusion that the targeted user recommendation system, and such user back into the model test to verify the target User's accuracy.

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Research on User Behavior Analysis Model of Financial Industry in Big Data Environment

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) ◽

10.1109/icaica50127.2020.9182369 ◽

2020 ◽

Author(s):

Junfeng Mei ◽

Ying Chen ◽

Taoli Ye ◽

Chenglong Huang ◽

Huiru Ye

Keyword(s):

Big Data ◽

Behavior Analysis ◽

User Behavior ◽

Analysis Model ◽

Financial Industry ◽

User Behavior Analysis ◽

Data Environment

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Design of user behavior analysis model of e-commerce website based on Spark

10.1109/icisce50968.2020.00112 ◽

2020 ◽

Author(s):

Xiumei Wen ◽

Yuxuan Han ◽

Jianglong Fu ◽

Panying Li ◽

Fanxing Meng

Keyword(s):

Behavior Analysis ◽

User Behavior ◽

Analysis Model ◽

User Behavior Analysis

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Design and Implementation of a Online Network User Behavior Analysis System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.566.707 ◽

2012 ◽

Vol 566 ◽

pp. 707-711

Author(s):

Song Li Hou ◽

Yuan Li

Keyword(s):

Behavior Analysis ◽

Real Time ◽

User Behavior ◽

Analysis Model ◽

Real Time Monitoring ◽

Online Analysis ◽

User Behavior Analysis ◽

Design And Implementation ◽

Internal Network ◽

Analysis System

By research of the current network traffic idenfication methods and typical network user behavior analysis methods，a online network user behavior analysis model has been designed and implemented. In order to achieve internal network user behavior real-time monitoring and online analysis purposes.

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The Application of User Behavior Analysis by Improved K-means Algorithm Based on Hadoop

Revista de la Facultad de Ingeniería ◽

10.21311/002.31.7.11 ◽

2016 ◽

Keyword(s):

Behavior Analysis ◽

User Behavior ◽

User Behavior Analysis

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Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190404160733 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1008-1019

Author(s):

N. Vijayaraj ◽

T. Senthil Murugan

Keyword(s):

Resource Allocation ◽

Behavior Analysis ◽

User Behavior ◽

Low Cost ◽

Cloud Services ◽

Online Auction ◽

Analysis Model ◽

Efficient Resource ◽

User Centric ◽

Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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