Abstract
This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.
Highlights
Based on the Internet and wireless communication technology, we can watch videos, browse the web, read online, listen to music, communicate, and shop online anytime and anywhere
Even though search engines such as Google and Baidu can satisfy most people’s needs directly through keyword search, with the improvement of people’s quality of life, it is difficult to rely on search engines to provide more personalized and customized user needs; personalized recommendation systems arise from such market demand [1]. e system can recommend products that users may like and be interested in based on their daily browsing history, search history, and other related information, which greatly reduces the tediousness of searching for interesting contents in huge data, and improves their traffic and time of using related system software, and the personalized recommendation system plays a complementary role for both enterprises and users
As the discipline of complex networks continues to evolve, the impact of network structure on recommender systems is of strong research interest
Summary
Based on the Internet and wireless communication technology, we can watch videos, browse the web, read online, listen to music, communicate, and shop online anytime and anywhere. One of the greatest significance of user search behavior analysis which is the fundamental purpose of this thesis is to use relevant data analysis algorithms to conduct in-depth mining of user log information, to derive the behavioral intent of users searching websites, click patterns, and time patterns, and to learn about users’ needs, interests, and user portraits If these general laws are combined with the marketing strategy of e-commerce, can we deeply understand the users and improve the quality of products and services, but we can analyze the potential users of products, target advertising to users, provide a reference basis for focusing on recommending certain products, improve the service system of personalized recommendation on websites, innovate the marketing model, and make the marketing strategy of e-commerce further improved.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
