Title :
A Study on the Granularity of User Modeling for Tag Prediction
Author :
Frias-Martinez, Enrique ; Cebrian, M. ; Jaimes, A.
Author_Institution :
Telefonica Res., Data Min. & User Modeling Group, Madrid
Abstract :
One of the characteristics of tag prediction mechanisms is that, typically, all user models are constructed with the same granularity. In this paper we hypothesize and empirically demonstrate that in order to increase tag prediction accuracy, the granularity of each user model has to be adapted to the level of usage of each particular user. We have constructed user models for tag prediction using association rules in Bibsonomy, a popular social bookmark and publication sharing system, at three granularity levels: (1) canonical, (2) stereotypical and (3) individual. Our experiments show that prediction accuracy improves if the level of granularity matches the level of participation of the user in the community (i.e., amount of tagging in Bibsonomy).
Keywords :
data mining; information analysis; user modelling; Bibsonomy; association rule; canonical granularity level; individual granularity level; publication sharing system; social bookmark; stereotypical granularity level; tag prediction; user modeling; Accuracy; Association rules; Data mining; Information retrieval; Intelligent agent; Libraries; Predictive models; Tagging;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-0-7695-3496-1
DOI :
10.1109/WIIAT.2008.67