基于聚类事务的档案聚合技术在电子商务推荐系统中的应用

Application of profile aggregations based on clustering transactions in E-commerce recommendation system

通常电子商务网站使用推荐系统为客户建议一些产品和为客户提供信息来帮助他们决定哪些产品是他们潜在要买的。研究人员已经应用包括web使用挖掘在内的许多方法来解决正在发展中的准确和有效的推荐系统的基本问题。本文提出在设计B2C推荐系统中应用一种在web使用挖掘中广泛使用的基于聚类事务的档案聚合(PACT)技术。应用这种技术本文实现了一个网上在线商店客户所属目标组的识别,并设计了一个推荐系统。通过使用这个推荐系统,客户的偏好和相关的产品信息是自动地从单击流(web使用数据)中获得的,不像其他的推荐方法仅仅是从购买记录中获得的。这样能为不同客户实时地推荐满足其偏好的个性化商品,保证了推荐质量,建立了良好的客户群体,提高了服务质量,加强了站点市场竞争力。

The E-commerce sites Recommendation systems them and help them to find out what kinds of the products can interact with the consumers, recommend products to they need most. Many different approaches including web usage mining have been applied to solve the basic problem of developing accurate and efficient recommendation systems. This paper presents the application of Profile Aggregations based on Clustering Transactions （PACT）, a widely used techniques in web usage mining, in designing a BtoC E-commerce recommendation system. We propose a target group identification of online grocer customer by applying the proposed methodology, and develop a recommendation system, by which the customer preference and the product association are automatically learned from click-stream （web usage data） unlike other recommendation methodologies which learn these information from purchase records only. So the customer personalized demand was satisfied much more better, which ensure the recommendation quality, establish favorable client group, improve service quality, and enhance the sites market competition ability greatly.