基于Web挖掘的个性化推荐系统研究

Xiaosheng Yu, Shanzun Sun
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引用次数: 11

摘要

越来越多的电子商务网站提供不同价格的产品,这使得消费者很难找到他们想要的产品和服务。为了克服信息过载,提出了个性化推荐系统,向消费者推荐产品,为消费者提供信息,帮助他们决定购买哪些产品。个性化推荐系统可以通过将浏览者转化为买家、增加交叉销售和建立忠诚度来提高电子商务销售,从而防止客户流失。本文分析了web挖掘的定义,介绍了两种关键的个性化推荐技术,建立了基于web挖掘的个性化推荐系统框架。在论文的最后一部分,作者对一个实例系统Amazon.com进行了研究。
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Research on Personalized Recommendation System Based on Web Mining
More and more E-commerce websites provide the products with different price which made it hard for consumers to find the products and services they wanted. In order to overcome the information overload, personalized recommendation systems were proposed to suggest products and to provide consumers with information to help them decide which products to purchase. Personalized recommendation systems can enhance E-Commerce sales by converting browsers into buyers, increasing cross-sell and building loyalty to prevent customers losing. In this paper the definition of web mining is analyzed, two key personalized recommendation technologies are introduced, a framework of personalized recommendation system based on web mining has been established. In the last part of the paper the author researches on an example system: Amazon.com.
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