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Research on Technology of Personalized Recommendation in E-business Based on Data Mining

Author: YangFan
Tutor: JiangJianGuo
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: E-commerce web data mining Collaborative Filtering Recommended system
CLC: TP311.13
Type: Master's thesis
Year: 2008
Downloads: 445
Quote: 5
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With the popularity of the Internet , e-commerce has made great progress because of its fast and convenient , high efficiency , low cost . Commerce sites have been expanding , the structure becomes more complex . For customers , in the face of a large number of product information , often unable to find the goods they need , how to effectively improve the attractiveness of its website ; For businesses , enhance customer service levels , get more business benefits are the problems to be solved . E-commerce recommendation system is an effective means to solve this problem . Firstly, the current recommendation system theory and key technology in-depth analysis and research , analyzed the problems in the existing recommendation system . The establishment of an e-commerce recommendation system architecture , and a detailed description of the function of each module in the system , structure as well as the realization of the algorithm . In order to improve the accuracy of the personalized recommendation system based on the customer preferences page the implicit collaborative filtering algorithm ( CPPICF ) , focuses on the customers nearest neighbor problem in collaborative filtering to achieve the objective rating of information , better solution data sparsity problem . The recommended results deviation for the system , this paper design recommendation algorithm CRF and evaluate the availability and effectiveness of the algorithm , enhanced customer satisfaction , and improve the quality of the recommended . Consider to CPPICF algorithm in the large amount of data computing efficiency is not high ; recommendation results in the form of multi- hyperlink recommendation , personalization is not obvious . So , the follow - up work on the one hand to improve the algorithm , customers browse the pages of time into a strong representation of the data is less model ; the other hand , is the study of how to combine a personal interest in the characteristics of the client , using the appropriate form interface ( such as floating ads , etc.) to provide customers with the recommendation, in order to improve the degree of personalization .

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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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