摘要
为了提高个性化推荐的质量,简化推荐规则生成过程中相关参数的设置,讨论了应用于个性化推荐中的关联规则的性质。提出了一种新的存储结构FSTree,并在这种存储结构上探讨了基于前项不定长的关联规则挖掘算法,通过实验证明了该算法的准确率和综合测度。
To improve quality of personalized recommendation and simplify the preference setup in generating recommendation rules,the characteristics of the association rule for personalized recommendation are discussed.We discuss a new storage structure named FSTree.Then based on FSTree,we discuss a Web usage mining with Based-Front Item of unconfirm Length,The theoretic analysis and experiment results on the algorithm show that the method has higher precision and F-measure of recommendation.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第7期174-177,共4页
Computer Engineering and Applications
