基于多级客户模型的个性化推荐机制

个性化是未来Web智能系统的一大特征．为了实现商品的个性化推荐，提出了一种新的基于多级客户模型的推荐系统机制，它由数据准备、模型学习、推荐集的生成和智能过滤四个子过程构成．该机制借助于多级客户模型从客户的购物需求、偏爱特征和消费能力三方面捕获客户的实际需求，从而实现了一种深层次的个性化推荐，改善了推荐效果．

Personalization Recommendation Mechanism Based on Multilevel Customer Model

Personalization is a major characteristic of Web intelligent system for the future. To provide personalized commodity recommendation, this paper presents a new recommendation mechanism based on multilevel customer model, which consists of data preprocessing, model learning, recommendation set generating, and intelligent filtering. The shopping model of customer is learned from customer's shopping transactions firstly. By means of probability inference, the recommendation engine generates respective recommendation set for each online customer. Then based on preference and consumption model of customer, the commodities in the recommendation set are customized and orientated, which realizes more personalized commodity recommendation. In our approach, the system captures customer's practice needs from three aspects: shopping demands, preference characters and purchasing power, thus strengthens the degree of personalization in recommendation and improves the effectiveness of recommendation.