{"links":{},"id":2009795,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02009795","sets":["1164:5064:1770876432362:1779319553198"]},"path":["1779319553198"],"owner":"80578","recid":"2009795","title":["大規模言語モデルを用いた会話内容に基づく音楽推薦および自動再生システムとプロンプト設計考察"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2026-05-29"},"_buckets":{"deposit":"421ede1e-8126-452d-afa0-0f8e5af2d920"},"_deposit":{"id":"2009795","pid":{"type":"depid","value":"2009795","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"大規模言語モデルを用いた会話内容に基づく音楽推薦および自動再生システムとプロンプト設計考察","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大規模言語モデルを用いた会話内容に基づく音楽推薦および自動再生システムとプロンプト設計考察","subitem_title_language":"ja"},{"subitem_title":"Music Recommendation and Automatic Playback System Based on Conversation Content Using Large Language Models and Analysis of Prompt Design","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"MUS","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2026-05-29","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京都立大学"},{"subitem_text_value":"東京都立大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Metropolitan University","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Metropolitan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/2009795/files/IPSJ-MUS26146022.pdf","label":"IPSJ-MUS26146022.pdf"},"date":[{"dateType":"Available","dateValue":"2028-05-29"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MUS26146022.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"21"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1151fb13-e43e-44bb-aa5c-b8a773b96310","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2026 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山本,晴菜"}]},{"creatorNames":[{"creatorName":"馬場,哲晃"}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Haruna Yamamoto","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tetsuaki Baba","creatorNameLang":"en"}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438388","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8752","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，複数人の会話内容に基づき，その場の雰囲気に適した楽曲を推薦・自動再生するシステムを提案する．マイクを通じて取得したリアルタイムの音声データをテキスト化し，大規模言語モデル（LLM）を用いて会話から文脈・感情・トーンといった情報を抽出する．これらに基づきSpotify APIを利用して，状況に即した楽曲推薦を行うシステムを構築した．プロンプト設計が推薦結果に与える影響を検討するため，3種類の会話シナリオを用いた予備的な比較実験を実施した．実験の結果，会話のトーンに着目したプロンプトは，全シナリオにおいて評価のバラつきが少なく，一貫して高い適合度が得られる傾向が見られた．一方で，雑談では話題の文脈重視，議論では進行を妨げず「場の状態」に適応する選曲が重視されるなど，状況により有効な入力軸が異なる可能性が示唆された．また，全要素を統合したプロンプトは推薦が平均化され，適合度の評価は高まらない傾向も確認された．本研究知見を用いることで，友人との雑談や話し合いにおいて，会話を妨げることなく状況に調和したバックグラウンド音楽を自動提示するサービスの実現が期待される．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告音楽情報科学（MUS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2026-05-29","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"22","bibliographicVolumeNumber":"2026-MUS-146"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"created":"2026-05-21T06:17:28.155585+00:00","updated":"2026-05-21T06:17:38.333387+00:00"}