一种基于后项不定长关联规则的Web个性化推荐方法

A Web Personalized Recommendation Method Based on Uncertain Consequent Association Rules

1引言 随着Internet的发展,电子商务、远程教育等各种基于网络的服务也得到迅猛发展.实现网上信息个性化已经成为一种热点.

Web usage mining plays an important part in supporting personalized recommendation on Web and association rule uncovers the interesting relations among items hidden in data. The paper gives an idea of association rule merging-deleting based on the analysis of association rule characteristics and implements it in the rule preparation before the Web personalized recommendation. Furthermore, based on the comparisons in precision, coverage and F1 of recommendation system and the rule numbers used in three kinds of association rules, a Web personalized recommendation method based on uncertain consequent is put forward. After integrative analysis of several recommendation methods, the method given in the paper can be thought as a good selection. At last several page-weighted techniques are introduced in the paper.