Collaborative Filtering Algorithm for Recommendation System of Improvement Based on Big Data Environment

With the rapid development of Internet and information technology, the exponential growth of information has attracted a lot of concern thesedays. Big data processing is particularly important. Recommendation system appears a good solution inadequacies of search engines, it is in addition based on keywords entered by the user to obtain information, but also with the user's social circle, as well as search history records, for users personalized recommendations services, and to establish a long and constant user interaction relations, not only improve customer loyalty, but also for the producers to create a good and reliable information platform for big data processing to achieve a win-win.

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Semantic Enriched Category Recommendation System for Large-Scale Emails Exploiting Big Data Processing Technologies

2015 IEEE 39th Annual Computer Software and Applications Conference ◽

10.1109/compsac.2015.96 ◽

2015 ◽

Author(s):

Jae-Ik Kim ◽

Kyung-Wook Park ◽

Hyung-Rak Jo ◽

Dong-Ho Lee

Keyword(s):

Big Data ◽

Data Processing ◽

Large Scale ◽

Recommendation System ◽

Big Data Processing ◽

Processing Technologies ◽

Enriched Category

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High-Performance Geospatial Big Data Processing System Based on MapReduce

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100399 ◽

2018 ◽

Vol 7 (10) ◽

pp. 399 ◽

Cited By ~ 10

Author(s):

Junghee Jo ◽

Kang-Woo Lee

Keyword(s):

Big Data ◽

Spatial Analysis ◽

Data Processing ◽

Spatial Data ◽

High Performance ◽

Rapid Development ◽

Processing System ◽

Data Processing System ◽

Seamless Integration ◽

Big Data Processing

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

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Application Strategy of Big Data Processing Technology in Intelligent Transportation

Journal of Electronic Research and Application ◽

10.26689/jera.v5i1.1892 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Yaohua Xie ◽

Xin Zhou

Keyword(s):

Big Data ◽

Data Processing ◽

Traffic Management ◽

Traffic Accidents ◽

Rapid Development ◽

Intelligent Transportation ◽

Processing Technology ◽

Internet Technology ◽

Big Data Processing ◽

Artificial Intelligence Technology

With the rapid development of China’s society and economy, the process of urbanization has been accelerated, and the transportation system has become more complicated, especially the frequent occurrence of traffic accidents, traffic congestions, and environmental pollution. In the context of the rapid development of Internet technology, digital technology, artificial intelligence technology, etc. We apply them to traffic management as effective ways to improve China’s traffic operation management. Based on big data processing technology, this paper discusses its application strategy in intelligent transportation, in hope of serving as a reference.

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The Application of Cloud Based on Latent Factor Algorithm in Personalized Recommendation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.2288 ◽

2013 ◽

Vol 411-414 ◽

pp. 2288-2291

Author(s):

Jian Xi Peng ◽

Zhi Yuan Liu

Keyword(s):

Big Data ◽

Collaborative Filtering ◽

Real Time ◽

Recommendation System ◽

Personalized Recommendation ◽

Massive Data ◽

Latent Factor ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Data Environment

Recommendation system is a commercial marketing method. What more, the system could increase adhesion and satisfaction of consumers to the website which brings great commercial benefit to electronic commerce. But with big data ages coming, it makes a great challenge to real-time recommendation system. As for latent factor class collaborative filtering algorithm, a distributed constructed latent factor algorithm based on cloud is presented in this paper. The algorithm could keep collaborative filtering in good recommendation and ensure the real time in massive data environment. The simulation shows that the algorithm could achieve the recommendation efficiently and quickly. High speedup and scalability are proved.

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