毕业论文移动版

摘要随着互联网的发展，科技的进步，信息内容也变得复杂多样，用户很难从大量的信息中筛选出对自己有用的信息，如何从海量的数据中获取用户感兴趣的内容已经成为了当今时代的要求。

在此基础上，本文提出了一种基于用户行为分析的B2C网站个性化推荐方法，目的是为了对某类用户进行产品的个性化推荐。文章针对的是新一站保险网，对新一站保险产品和用户进行分析，利用R语言和sql语句，先对用户进行分类，再将产品匹配到用户类中，实现产品的个性化推荐。83240

文章具体讲述了个性化推荐的背景和现状，介绍了现阶段盛行的一些推荐方法，主体部分阐述了此次研究的整个过程，包括研究的知识准备阶段，研究前的数据分析阶段，以及数据获取阶段，还有数据建模过程，对建模后的结果进行分析，提出有效结论。最后，针对分析的结论，阐述了个性化推荐方法在实际生活中的应用，进行了展望。

毕业论文关键字:用户行为 个性化推荐 协同过滤 数据挖掘 决策树

毕业设计说明书外文摘要

Title  Research on Personalized Recommention Method of  B2C Website Based on User Behavior Analysis         

Abstract With the development of Internet, the progress of science and technology, information content is becoming complicated, users are difficult to screen out from a lot of information about their useful information。How to obtain from the vast amounts of data of the interesting content has become a requirement in today's world。 

On this basis, this paper puts forward a B2C website personalized recommendation method based on user behavior analysis。 The purpose is to make a personalized recommendation for a certain type of users。 This article is aimed at the new station insurance network, to analyze a new insurance products and users, using R language and SQL statements, classifying the user first, then match the product to the user class, achieve the personalized recommendation products。 

Article specific about the background and status quo of personalized recommendation。 At the same time, it also introduced some kinds of present popular recommended method。 The main part expounds the whole process of the research, including research knowledge preparation stage before the research data analysis phase, as well as the data acquisition phase, and the data modeling process, the analyzes of modeling results ,the effective conclusions of putting forward。 Finally, in view of the analysis conclusion, this paper expounds the personalized recommendation method applied in the practical life, and the prospect are put forward。 

Keywords：user behavior    personalized recommendation

collaborative filtering    data mining    decision tree

目录

1  绪论 1

1。1  背景及意义 1

1。2  文献综述 2

1。2。1  用户行为分析综述 2

1。2。2  个性化推荐综述 3

1。3  本文组织结构 5

2  个性化推荐的相关算法 7

2。1  协同过滤算法 7

2。2  基于关联规则的推荐算法 8

2。3  基于用户统计信息的个性化推荐算法 8

3  研究过程 10

3。1  研究的准备阶段 (责任编辑：qin)