TY - GEN
T1 - Improving the quality of tags using state transition on progressive image search and recommendation system
AU - Du, Wen Hau
AU - Rau, Jer Wei
AU - Huang, Jen Wei
AU - Chen, Yung Sheng
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In recent years, tagging methods have been widely used on the Web for multimedia search and recommendation systems. Social tags are the keywords annotated by users to the multimedia objects. These tags contain the information which allows multimedia objects to be located and classified. Unfortunately, irrelevant information and noise are frequently included in such tags, especially when we use social tagging systems. Imperfect as they may be, social tags are still a significant source of human-generated contextual knowledge. We present the Waking And Sleeping (WAS) algorithm to overcome the obstacle of noisy tags. WAS refines social tags of each image and assigns each tag into either the waking state or the sleeping state. With WAS, the recommendation system not only enhances the accuracy of the recommendation results based on tags with higher quality but also reduces the consumption of computational resources. The experimental results demonstrate that the proposed approach is superior to compared methods.
AB - In recent years, tagging methods have been widely used on the Web for multimedia search and recommendation systems. Social tags are the keywords annotated by users to the multimedia objects. These tags contain the information which allows multimedia objects to be located and classified. Unfortunately, irrelevant information and noise are frequently included in such tags, especially when we use social tagging systems. Imperfect as they may be, social tags are still a significant source of human-generated contextual knowledge. We present the Waking And Sleeping (WAS) algorithm to overcome the obstacle of noisy tags. WAS refines social tags of each image and assigns each tag into either the waking state or the sleeping state. With WAS, the recommendation system not only enhances the accuracy of the recommendation results based on tags with higher quality but also reduces the consumption of computational resources. The experimental results demonstrate that the proposed approach is superior to compared methods.
UR - http://www.scopus.com/inward/record.url?scp=84872424166&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872424166&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2012.6378289
DO - 10.1109/ICSMC.2012.6378289
M3 - Conference contribution
AN - SCOPUS:84872424166
SN - 9781467317146
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3233
EP - 3238
BT - Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
T2 - 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Y2 - 14 October 2012 through 17 October 2012
ER -