Paper Info
Title
Mining same-taste users with common preference patterns for ubiquitous exhibition navigation
Abstract
In a ubiquitous exhibition, an intelligent navigation service that can provide booths' information, recommend interesting booths and plan touring path is required for both visitors and vendors. The preference mining module is the kernel. This paper proposes a group-based user preference pattern mining method, which can be implemented as a preference mining module in this service. When the visiting traces that imply the preference of users are recorded, the method discovers user preference patterns with high representativeness and high discrimination from the historical visiting logs. According to the discovered model, collaborative recommendation can be accomplished, and then the intelligent navigation service can plan personalized touring path based on the recommendation lists. For demonstrating the performance of the proposed method, we engage some experiments, and then indicate the characteristics of the proposed method.
Year
DOI
Venue
2012
10.1007/978-3-642-28493-9_44
ACIIDS (3)
Keywords
Field
DocType
ubiquitous exhibition navigation,mining method,collaborative recommendation,high discrimination,recommendation list,user preference pattern,preference mining module,common preference pattern,intelligent navigation service,high representativeness,group-based user preference pattern,mining same-taste user
Kernel (linear algebra),Data mining,World Wide Web,Computer science,Representativeness heuristic,Exhibition,Cluster analysis,Multimedia
Conference
Volume
ISSN
Citations 
7198
0302-9743
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Shin-Yi Wu141431.59
Li-Chen Cheng222615.09