An Article Kansei Retrieval System Combining Recommendation Function and Interaction Design

• Item type: Journal(1)
• 公開日: 2012-05-15
• タイトル: An Article Kansei Retrieval System Combining Recommendation Function and Interaction Design
• タイトル / 言語: en
• タイトル: An Article Kansei Retrieval System Combining Recommendation Function and Interaction Design
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Special Issue on Theory and Application of Intelligent Information Technology] personalization, Kansei information processing, human computer interaction, Web retrieval, intelligent user interface
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduated Schools of Advanced Science and Engineering, Waseda University
• 著者所属: Faculty of Science and Engineering, Waseda University
• 著者所属: Faculty of Science and Engineering, Waseda University
• 著者所属(英): en
• 著者所属(英): Graduated Schools of Advanced Science and Engineering, Waseda University
• 著者所属(英): en
• 著者所属(英): Faculty of Science and Engineering, Waseda University
• 著者所属(英): en
• 著者所属(英): Faculty of Science and Engineering, Waseda University
• 著者名: Yuichi, Murakami; Shingo, Nakamura; Shuji, Hashimoto
• 著者名(英): Yuichi, Murakami; Shingo, Nakamura; Shuji, Hashimoto
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In most article retrieval systems using Kansei words there exists a gap between user's Kansei and the system's Kansei model. Therefore, it is not always easy to retrieve the desirable articles. The purpose of this paper is to bridge this gap not to put a strain on users by combining the recommendation function and interaction design with four features. First, users can retrieve intuitively as the system visualizes retrieval space consisting of a torus type SOM (Self Organizing Maps). Second, users can find the most desirable article in any case by elimination methods to delete undesirable articles pointed by the user. Third, neural networks in the system learn user's Kansei based on the most desirable article to improve the retrieval accuracy. Fourth, users can search articles by arbitrary Kansei words, and can edit retrieval criteria as they please. In the evaluation experiments, the authors took actual paintings as the articles, and evaluated usability (effectiveness, efficiency and satisfaction), novelty and serendipity. These results were led by the synergetic effects of the recommendation function and interaction design.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.20(2012) No.3 (online); DOI http://dx.doi.org/10.2197/ipsjjip.20.548; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In most article retrieval systems using Kansei words there exists a gap between user's Kansei and the system's Kansei model. Therefore, it is not always easy to retrieve the desirable articles. The purpose of this paper is to bridge this gap not to put a strain on users by combining the recommendation function and interaction design with four features. First, users can retrieve intuitively as the system visualizes retrieval space consisting of a torus type SOM (Self Organizing Maps). Second, users can find the most desirable article in any case by elimination methods to delete undesirable articles pointed by the user. Third, neural networks in the system learn user's Kansei based on the most desirable article to improve the retrieval accuracy. Fourth, users can search articles by arbitrary Kansei words, and can edit retrieval criteria as they please. In the evaluation experiments, the authors took actual paintings as the articles, and evaluated usability (effectiveness, efficiency and satisfaction), novelty and serendipity. These results were led by the synergetic effects of the recommendation function and interaction design.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.20(2012) No.3 (online); DOI http://dx.doi.org/10.2197/ipsjjip.20.548; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 53, 号 5, 発行日 2012-05-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764