Title :
Mining Users´ Opinions Based on Item Folksonomy and Taxonomy for Personalized Recommender Systems
Author :
Liang, Huizhi ; Xu, Yue ; Li, Yuefeng
Author_Institution :
Fac. of Sci. & Technol., Queensland Univ. of Technol., Brisbane, QLD, Australia
Abstract :
Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users´ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users´ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.
Keywords :
Internet; data mining; pattern classification; recommender systems; Web 2.0; data mining; information source; item classifications; item descriptions; item folksonomy; item taxonomy; opinion mining; personalized recommender systems; tag information; Folksonomy; Opinion Mining; Personalization; Recommender Systems; Tags; Taxonomy;
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
DOI :
10.1109/ICDMW.2010.163
