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Application of Association Rule Mining in E-commerce Recommendation System

Author: LinJiaXiong
Tutor: HuangZhan
School: Jinan University
Course: Applied Computer Technology
Keywords: Personalized Recommendation Association rules Conceptual level Evolution rules
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 216
Quote: 1
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Abstract


The popularity of the Internet and e-commerce caused by the rapid development of information overload , massive product information is difficult to find the necessary products . So, how in the vast amounts of product information quickly and efficiently find the needed goods become the outstanding problems of the current e - commerce development , e-commerce recommendation system came into being . Current e-commerce recommendation system in actual use is still quite immature , there are data sparsity , system scalability and accuracy and low , so that the the Recommend effect is not prominent, and failed to fully meet the needs of users . E - commerce recommendation system has been great development in the theory and practice . Association Rules Recommendation technology is a personalized recommendation technology applications in e - commerce recommendation system is more successful . However , in practical applications , the presence of this technique is difficult and the resulting association rules discovered association rules render static problems . To solve these problems , the introduction of the concept hierarchy and the establishment of the the evolution rule set new ideas to improve it , and design a recommendation model based on the evolution of the rule set . Order to better study personalized recommendation system , the online clothing store in this area as a specific application background based on the the evolution rule set the recommended model , use Myeclipse7.0 platform to achieve the recommended model . Finally , use the hit rate the evaluation standard experimental test of the system , the experimental results show that the recommended model has been significantly improved the accuracy of the recommended results .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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