TY  - JOUR
AU  - Xu, Yanfei
AU  - Zhang, Liang
AU  - Liu, Wei
T1  - Cubic Analysis of Social Bookmarking for Personalized Recommendation
JO  - Frontiers of WWW Research and Development - APWeb 2006
PY  - 2006/
VL  - 
IS  - 
SP  - 733
EP  - 738
UR  - http://dx.doi.org/10.1007/11610113_66
M3  - 
KW  - tagging
KW  - taggingsurvey
KW  - recommender
KW  - summerschool
KW  - social
KW  - folksonomy
KW  - bookmarking
KW  - kdubiq
KW  - toread
L1  - 
SN  - 
N1  - SpringerLink - Book Chapter
N1  - 
AB  - Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER  -
ER  -