An article retrieval support system that learns user's Kansei

Yuichi Murakami, Shingo Nakamura, Shuji Hashimoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010
    Pages32-37
    Number of pages6
    DOIs
    Publication statusPublished - 2010
    Event1st International Conference on User Science and Engineering 2010, iUSEr 2010 - Shah Alam
    Duration: 2010 Dec 132010 Dec 15

    Other

    Other1st International Conference on User Science and Engineering 2010, iUSEr 2010
    CityShah Alam
    Period10/12/1310/12/15

    Fingerprint

    Self organizing maps
    Painting
    Neural networks
    Experiments

    Keywords

    • Kansei
    • Neural networks
    • Retrieval support system
    • Torus SOM

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Human-Computer Interaction
    • Software

    Cite this

    Murakami, Y., Nakamura, S., & Hashimoto, S. (2010). An article retrieval support system that learns user's Kansei. In Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010 (pp. 32-37). [5716718] https://doi.org/10.1109/IUSER.2010.5716718

    An article retrieval support system that learns user's Kansei. / Murakami, Yuichi; Nakamura, Shingo; Hashimoto, Shuji.

    Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010. 2010. p. 32-37 5716718.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Murakami, Y, Nakamura, S & Hashimoto, S 2010, An article retrieval support system that learns user's Kansei. in Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010., 5716718, pp. 32-37, 1st International Conference on User Science and Engineering 2010, iUSEr 2010, Shah Alam, 10/12/13. https://doi.org/10.1109/IUSER.2010.5716718
    Murakami Y, Nakamura S, Hashimoto S. An article retrieval support system that learns user's Kansei. In Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010. 2010. p. 32-37. 5716718 https://doi.org/10.1109/IUSER.2010.5716718
    Murakami, Yuichi ; Nakamura, Shingo ; Hashimoto, Shuji. / An article retrieval support system that learns user's Kansei. Proceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010. 2010. pp. 32-37
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