Solving a start-up problem of recommender systems by employing transfer learning
| Project/Area Number |
21500154
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
KAMISHIMA Toshihiro 独立行政法人産業技術総合研究所, ヒューマンライフテクノロジー研究部門, 主任研究員 (50356820)
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| Co-Investigator(Kenkyū-buntansha) |
SHOTARO Akaho 独立行政法人産業技術総合研究所, ヒューマンライフテクノロジー研究部門, 研究グループ長 (40356340)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2011: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2010: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2009: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 知識発見とデータマイニング / 推薦システム / 転移学習 / recommender system / transfer learning / 協調フィルタリング |
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| Research Abstract |
In this project, we planed to address a start-up problem, which means the poor prediction accuracy of recommendation systems for new users or new items. For this purpose, we developed a TrBagg algorithm, which utilizes information of other users' data. We tested this algorithm on a tag recommendation task and succeeded to improve the prediction accuracy. We also tried to settle other problems related to recommender systems.
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Report
(4 results)
Research Products
(28 results)
