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Research on Personalized Recommendation System Based on Web Log

Author: WangLinLin
Tutor: ShiBing
School: Shandong University
Course: Computer Software and Theory
Keywords: Personalized Recommendation System Web log WeightedAssociation Rules Weight Collaborative filtering
CLC: TP311.13
Type: Master's thesis
Year: 2012
Downloads: 192
Quote: 0
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Abstract


With the development of e-commerce in full swing, the scale of e-commerce site is unceasing expansion,and goods on the net is more and more abundant. Although the users have greater choice, but faced with more and more of the reference information don’t know how to choose. How to according to the preferences of the user to provide users with convenient, accurate recommendations,become more and more people research content.In order to better satisfy every customer’s demand, according to users preference to search goods and improve competitiveness, personalized commodity recommend system came into being. Personalized products recommended systems are built on the basis of a senior business intelligence platform in the massive data mining to help an e-commerce site to its customers shopping provide fully customized decision support and information services. Shopping site recommended for the customer to recommend commodities, automatic completion of individual choice of goods process, satisfies the customer the individual requirements.This paper mainly studies the weighted association rules based on Web log and collaborative filtering algorithm in personalized recommendation system of the application. This paper mainly content as follows:(1) introduces the personalized recommendation system development status and key technology;(2) according to Web log data format, excavate the implicit information used in personalized recommendation system, to make up for e-commerce sites users to display information in a show of defect;(3) according to the characteristics of the Web log and e-commerce sites personalized recommendation the characteristics of the system, this paper selects the weighted association rules and collaborative filtering algorithm for data mining,and the improved the algorithm is applied to personalized recommendation system, in a personalized system according to user interests and recommended according to nearest neighbor prediction recommend a combination of recommendation;(4) according to the characteristics of e-commerce sites,the weight calculation is improved.Because the e-commerce sites’data is huge,we use digraph manner scanning, put forward based on directed graph of the weight of association rules, use the Web log information for collaborative filtering algorithm reconstruction, and put forward based on the Web log clustering algorithm for collaborative filtering collaborative filtering data sparse solution to improve.

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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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