Collaborative Filtering Algorithm For Big Data Applications in E-Commerce

The traditional preaching way of imparting knowledge can only stifle children’s imagination, creativity, and learning initiative a little bit, which is harmful to children’s healthy and happy growth. This paper combines big data technology to evaluate the effect of game teaching method in preschool education, analyzes the teaching effect of game teaching method in preschool education, and combines big data technology to find problematic teaching points. Based on the collaborative filtering algorithm of preschool children, this paper estimates the current preschool children’s score for the game by referring to the scores of neighbor preschool children on the predicted game and constructs an intelligent model. Finally, this paper combines experimental research to verify the model proposed in this paper. From the experimental research, it can be seen that the method proposed in this paper has a certain effect.

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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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Big Data Visualization Collaborative Filtering Algorithm Based on RHadoop

International Journal of Distributed Sensor Networks ◽

10.1155/2015/271253 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 12

Author(s):

Lijun Cai ◽

Xiangqing Guan ◽

Peng Chi ◽

Lei Chen ◽

Jianting Luo

Keyword(s):

Big Data ◽

Collaborative Filtering ◽

Data Storage ◽

Data Visualization ◽

Integrated Model ◽

Algorithm Analysis ◽

Analysis Tool ◽

Filtering Algorithm ◽

Big Data Visualization ◽

Collaborative Filtering Algorithm

With the rapid growth of various data, it is becoming increasingly important to extract useful information from big data. While the analysis tools of big data visualization is very rare, in this paper, we propose a new big data visualization algorithm analysis integrated model. The model integrates the processing of big data and the visualization of data as a whole. It is a good analysis tool of timely big data visualization. We use hadoop_1.X as the data storage and use R as the compiler environment in the model. If you are skilled in R, it is easy to design kinds of paralleling algorithms, and analyze and process the kinds of big data. Secondly we design and implement a paralleled collaborative filtering algorithm with the model. Finally we analyze the various performance indicators with kinds of experiments. The indicators show that the model has good scalability and easy operability, and contains all the advantages of Map Reduce. In conclusion, the big data visualization algorithm analysis integrated model has high performance to process and visualize the big data.

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A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms

Open Physics ◽

10.1515/phys-2019-0102 ◽

2019 ◽

Vol 17 (1) ◽

pp. 966-974

Author(s):

Nan Yin

Keyword(s):

Big Data ◽

Data Analysis ◽

Collaborative Filtering ◽

Execution Time ◽

Recommendation System ◽

Big Data Analysis ◽

Analysis Method ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm

Abstract With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. Using recommendation system to accurately predict customers’ preferences for goods can solve the problem of information overload faced by users and improve users’ dependence on the network platform. Because the recommendation system based on collaborative filtering technology has the ability to recommend more abstract or difficult to describe goods in words, the research related to collaborative filtering technology has attracted more and more attention. According to the past research, in collaborative filtering algorithm, if Pearson correlation coefficient is used, errors will occur under special circumstances. In this study, the normal recovery similarity measure is used to modify the similarity value to correct the error value of a collaborative filtering recommendation algorithm. Based on this, a big data analysis method based on a modified collaborative filtering recommendation algorithm is proposed. This research implemented it in the cloud Hadoop environment, and measure the execution time with 2, 5 and 8 nodes. Then the research compared it with the execution time of a single machine, and analyze its speedup ratio and efficiency. The experimental results show that the execution time increases with the number of neighbors. When the number of nodes is 5 and 8, the execution time is greatly improved, which improves the efficiency of collaborative filtering algorithm and can cope with massive data in the future.

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