Abstract

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.

Highlights

  • With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms

  • According to the past research, in collaborative filtering algorithm, if Pearson correlation coefficient is used, errors will occur under special circumstances

  • The experimental results show that the execution time increases with the number of neighbors

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Summary

Introduction

Abstract: With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. The normal recovery similarity measure is used to modify the similarity value to correct the error value of a collaborative filtering recommendation algorithm. 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. 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. When the amount of data is so complex that the database system cannot store, calculate, process, and analyze the information that can be interpreted in a reasonable time, it is called big data. The second purpose of the research is to analyze the prediction results by using three algorithms: the Jaccard similarity coefficient, Pearson similarity and Normal recovery similarity measure

Recommendation System
Collaborative filtering algorithm for the recommendation system
User-based Collaborative Filtering Algorithms
Collaborative Filtering Program
Computation of Similarity
Pearson correlation coeflcient
Jaccard similarity coeflcient
Experimental environment and methods
Research results and analysis
Research conclusions

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