An Effective Technique for Personalization Recommendation Based on Access Sequential Patterns
Considering that personalization recommendation systems based on association rules suffer from some limitations that a lot of time is spent on matching current user session with all discovered patterns in patterns database, authors propose a new approach to build personalization recommendation system based on access sequential patterns discovered form usage data and highly compressed into a tree structure. During personalization recommendation stage we just need to intercept nearest access subsequence from current user session to match some sub paths of the tree. The speed of pattern matching is improved enormously, which satisfies the need of real-time recommendation better. The results of experiments show the proposed methodology can achieve better recommendation effectiveness.
Personalization Recommendation Sequential patterns Association Rules Web Usage Mining
TAN Xiaoqiu YAO Min Xu Miaojun
College of Information, Zhejiang Ocean University, Zhoushan Zhejiang, P.R.China College of Computer Science, Zhejiang University, Hangzhou Zhejiang, P.R.China
国际会议
2006 Asia-Pacific Services Computing Conference(IEEE亚太地区服务计算会议)
广州
英文
42-46
2006-12-12（万方平台首次上网日期，不代表论文的发表时间）