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Based on collaborative filtering technology, e-commerce personalization system

Author: WangLiang
Tutor: WangShiQing
School: East China Normal University
Course: Software Engineering
Keywords: E-commerce Personalization System Collaborative Filtering Portfolio Email
CLC: TP311.52
Type: Master's thesis
Year: 2008
Downloads: 176
Quote: 2
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Abstract


With each passing day the popularity of the network and information technology , e-commerce system to provide users with more and more choice , and its structure has become more complex, users often get lost in a large number of goods space , unable to find their own needed goods. E-commerce personalization system came into being in this case . E-commerce personalization system in the e-commerce platform Recommend to users based on the user 's data and behavior to help users find the necessary goods to the successful completion of the purchase process . Its broad application prospects at home and abroad . The user's attention . Collaborative filtering technology is a good way to e - commerce personalization system the earliest and one of the most successful technology , it is the basic idea : for the user to find what he really interested find user interest in his first , and then these users are interested in the content Recommend to users . Generally use the nearest neighbor technique, and calculating the distance between the user , and then using the nearest neighbor user of the target user for commodity Evaluation weighted evaluation values ??to predict the degree of liking of the target user on the particular commodity , the system according to this preference to the target user to recommend . The biggest advantage of the collaborative filtering recommendation object no special requirements , can handle unstructured complex objects , such as music, movies . Studied the current the personalized recommendation mainstream technology - collaborative filtering technology in-depth analysis of this algorithm sparse problems affecting the recommendation quality and impact user satisfaction Recommended integrity , and the introduction of a combination recommended collaborative filtering algorithm to improve the design of the simulation experiment to achieve the recommended strategy paper . The papers improved algorithm to study simulation experiments , after experimental verification , the improved algorithm are better than traditional algorithms recommended accuracy , integrity , diversity , especially in the sparse user evaluation data set reflects the good performance of the recommended . The paper designed an e-commerce personalization system framework , the completion of the common processes , provides a useful reference for the reality of e-commerce personalization system .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Development
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