An Effective Technique for Personalization Recommendation Based on Access Sequential Patterns

Authors:
Xiaoqiu Tan;Min Yao;Miaojun Xu
Affiliations:
Zhejiang Ocean University, China;Zhejiang University, China;Zhejiang Ocean University, China
Venue:
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
Year:
2006

Citing 0
Cited 4

RFID-Based Interactive Learning in Science Museums

UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Towards Privacy Compliant and Anytime Recommender Systems

EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Experiences with RFID-based interactive learning in museums

International Journal of Autonomous and Adaptive Communications Systems
Experiences with RFID-based interactive learning in museums

International Journal of Autonomous and Adaptive Communications Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.