{"created":"2025-01-19T01:38:17.763856+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236313","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"236313","title":["機械学習を用いたライブ配信の見どころの特徴分析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"c5b383c6-8cff-476f-a4a0-59f11518e95f"},"_deposit":{"id":"236313","pid":{"type":"depid","value":"236313","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いたライブ配信の見どころの特徴分析","author_link":["645908","645907"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いたライブ配信の見どころの特徴分析"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2024-03-01","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":"千葉工大"},{"subitem_text_value":"千葉工大"}]},"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/236313/files/IPSJ-Z86-2X-03.pdf","label":"IPSJ-Z86-2X-03.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-2X-03.pdf","filesize":[{"value":"277.6 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"69956126-d220-4074-b323-8dfefb1fa617","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"玉置, 昇太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"今野, 将"}],"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":"近年，ライブ配信が増加するにつれ，ライブ配信の見どころのみを抽出し編集した「切り抜き動画」の作成数も増加傾向にある．この切り抜き動画は，作成時にライブ配信を一通り見る必要がある．そのため既存研究でも，ライブ配信の面白い部分を選定について研究されていた．その手法は3つの特徴量を組み合わせた1つの指標を元に選定を行っていた．しかし， 用いた指標で面白い部分の値を表すには不十分という課題が残った．そこで，本研究では特徴量の数を増やし，機械学習である時系列分類モデルを用いてライブ配信見どころの選定を行う．具体的には，コメント数や音響特徴量をデータとし切り抜き動画で採用された部分の特徴を学習させる．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"962","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"961","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236313,"updated":"2025-01-19T09:18:32.903834+00:00","links":{}}