{"updated":"2025-01-19T16:27:51.475244+00:00","links":{},"created":"2025-01-19T01:15:35.604427+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214796","sets":["6504:10735:10806"]},"path":["10806"],"owner":"44499","recid":"214796","title":["バンディットアルゴリズムとメンション関係を利用した特定トピックに関する特定の地域のツイートの収集"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"0b8f89b6-ab41-46a3-933c-a8e9fb21c27b"},"_deposit":{"id":"214796","pid":{"type":"depid","value":"214796","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"バンディットアルゴリズムとメンション関係を利用した特定トピックに関する特定の地域のツイートの収集","author_link":["552238","552237","552236"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"バンディットアルゴリズムとメンション関係を利用した特定トピックに関する特定の地域のツイートの収集"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"データとウェブ","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大"},{"subitem_text_value":"筑波大"},{"subitem_text_value":"筑波大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/214796/files/IPSJ-Z83-6L-03.pdf","label":"IPSJ-Z83-6L-03.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-6L-03.pdf","filesize":[{"value":"377.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"5403332b-23d4-43c4-8c48-1e15c77f0754","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大森, 雄基"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北川, 博之"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"天笠, 俊之"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Twitter等のSNSの利用が拡大し，地域・トピックに沿った投稿から商圏分析等に利用する需要があるが，SNS情報は膨大・多様・不正確であり，計算機の制約とSNS運営による制限もあるため，プロファイルや投稿内容を用いてユーザの傾向を把握して効率的に投稿を収集する必要がある．本研究ではバンディットアルゴリズムのε-greedy法を改造し，三種の報酬を設定する．第一はユーザの活動地域推定とトピック文書類似度から算定した報酬．第二はメンション元ユーザの第一報酬を積算した報酬．第三は活動地域推定のみを利用した報酬である．これらを用いてユーザを選定し，地域・トピックに沿ったツイートを効率的に収集する仕組みを提案する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"382","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"381","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214796}