An article retrieval support system that learns user's Kansei

Yuichi Murakami, Shingo Nakamura, Shuji Hashimoto

    研究成果: Conference contribution

    1 引用 (Scopus)

    抜粋

    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.

    元の言語English
    ホスト出版物のタイトルProceedings - 2010 International Conference on User Science and Engineering, i-USEr 2010
    ページ32-37
    ページ数6
    DOI
    出版物ステータスPublished - 2010
    イベント1st International Conference on User Science and Engineering 2010, iUSEr 2010 - Shah Alam
    継続期間: 2010 12 132010 12 15

    Other

    Other1st International Conference on User Science and Engineering 2010, iUSEr 2010
    Shah Alam
    期間10/12/1310/12/15

    ASJC Scopus subject areas

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

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  • これを引用

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