{"created":"2025-01-19T01:29:09.579641+00:00","updated":"2025-01-19T11:25:11.567851+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229788","sets":["6504:11436:11439"]},"path":["11439"],"owner":"44499","recid":"229788","title":["ジオタグツイートとユーザ嗜好に基づく移動中の楽曲推薦手法の検証"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"671e9e63-843c-481a-a92d-94ab00dc2b05"},"_deposit":{"id":"229788","pid":{"type":"depid","value":"229788","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ジオタグツイートとユーザ嗜好に基づく移動中の楽曲推薦手法の検証","author_link":["618124","618127","618126","618125"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ジオタグツイートとユーザ嗜好に基づく移動中の楽曲推薦手法の検証"}]},"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":"2023-02-16","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":"京産大"},{"subitem_text_value":"山口大"},{"subitem_text_value":"関西学院大"},{"subitem_text_value":"京産大"}]},"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/229788/files/IPSJ-Z85-6N-08.pdf","label":"IPSJ-Z85-6N-08.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-6N-08.pdf","filesize":[{"value":"371.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"ce6bd51d-4e2d-4791-a101-8e5b3beea86c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"林, 伸宇"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"王, 元元"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"角谷, 和俊"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河合, 由起子"}],"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":"本研究は，移動中における楽曲推薦を目的とし，移動経路中に出現するスポットの雰囲気をツイートより抽出し，ユーザの視聴履歴より抽出した嗜好とスポットの雰囲気に類似した楽曲推薦手法を提案する．スポットはOSMのamenityカテゴリから抽出し，各スポットの周辺のSNSは位置情報付きツイートを収集し，スポットベクトルを生成する．また，歌ネットからアーティストとタイトルと歌詞を収集し，アーティストベクトルと視聴履歴よりユーザベクトルを生成する。これら3つのベクトルの類似度を算出し，楽曲を推薦する．本稿では，スポットの雰囲気抽出および楽曲推薦手法を検証する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"528","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"527","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":229788,"links":{}}