TY - GEN
T1 - An article retrieval support system that learns user's Kansei
AU - Murakami, Yuichi
AU - Nakamura, Shingo
AU - Hashimoto, Shuji
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Most of article retrieval systems using retrieval criteria of Kansei words have a gap between user's Kansei and system's Kansei model. Therefore, it is not always easy to retrieve the desired articles efficiently according to the user's preference. This paper proposed a system to retrieve the desired articles quickly and intuitively from the database. To achieve this aim, dimension of the retrieval space is compressed by a torus SOM (Self Organizing Maps), and a user can move in the retrieval space panoramically. A user can also choose an elimination method during search. By this method, the system estimates the significant Kansei parameters and makes the search more efficient. The system also has a function to eliminate the unselected articles and reduces the size of SOM. Additionally, the system learns the Kansei of individual user from the retrieval results by using neural networks. In evaluation experiments, we took actual painting as article, and confirmed the efficacy of the proposed method.
AB - Most of article retrieval systems using retrieval criteria of Kansei words have a gap between user's Kansei and system's Kansei model. Therefore, it is not always easy to retrieve the desired articles efficiently according to the user's preference. This paper proposed a system to retrieve the desired articles quickly and intuitively from the database. To achieve this aim, dimension of the retrieval space is compressed by a torus SOM (Self Organizing Maps), and a user can move in the retrieval space panoramically. A user can also choose an elimination method during search. By this method, the system estimates the significant Kansei parameters and makes the search more efficient. The system also has a function to eliminate the unselected articles and reduces the size of SOM. Additionally, the system learns the Kansei of individual user from the retrieval results by using neural networks. In evaluation experiments, we took actual painting as article, and confirmed the efficacy of the proposed method.
KW - Kansei
KW - Neural networks
KW - Retrieval support system
KW - Torus SOM
UR - http://www.scopus.com/inward/record.url?scp=79952858769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952858769&partnerID=8YFLogxK
U2 - 10.1109/IUSER.2010.5716718
DO - 10.1109/IUSER.2010.5716718
M3 - Conference contribution
AN - SCOPUS:79952858769
SN - 9781424490493
T3 - Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010
SP - 32
EP - 37
BT - Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010
T2 - 1st International Conference on User Science and Engineering 2010, iUSEr 2010
Y2 - 13 December 2010 through 15 December 2010
ER -