Context-aware drive music recommender systems
| Project/Area Number |
15K12151
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Web informatics, Service informatics
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| Research Institution | Ryukoku University (2016-2018)
Ritsumeikan University (2015) |
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Principal Investigator |
Oku Kenta 龍谷大学, 理工学部, 講師 (70551555)
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| Co-Investigator(Kenkyū-buntansha) |
山西 良典 立命館大学, 情報理工学部, 講師 (50700522)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 推薦システム / ルート推薦 / ドライブ景観推定 / ドライブ楽曲推薦 / 情報推薦システム / ドライブ風景推定 |
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| Outline of Final Research Achievements |
To develop context-aware drive music recommender systems, we tackled the following three themes: (1) Methods for featurizing music tracks based on acoustic features and lyrics features, (2) Methods for extracting contexts from scenery images, (3) Methods for extracting context-dependent music preferences. Finally, we experimentally developed context-aware drive music recommender systems based on the results of the themse (1), (2), and (3), and evaluated its usefulness.
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| Academic Significance and Societal Importance of the Research Achievements |
歌詞特徴量に基づく楽曲特徴化技術を確立することで，これまでにない新たな特徴量に基づいた楽曲検索や楽曲推薦を可能とし，当該分野の可能性が大きく広がることが期待できる．また，ドライブ時の風景画像から自動的にコンテキストを抽出する技術は，当該分野の課題解決に大きく貢献するものである．さらに，本研究で暗黙的なコンテキスト依存の楽曲嗜好抽出技術を確立することにより，当該分野の研究に大きく寄与する．
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Report
(5 results)
Research Products
(37 results)
