| Project/Area Number |
15H02701
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Multimedia database
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Miyazaki Jun 東京工業大学, 情報理工学院, 教授 (40293394)
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| Co-Investigator(Kenkyū-buntansha) |
波多野 賢治 同志社大学, 文化情報学部, 教授 (80314532)
中村 匡秀 神戸大学, システム情報学研究科, 准教授 (30324859)
欅 惇志 東京工業大学, 情報理工学院, 助教 (00733958)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
Fiscal Year 2017: ¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
Fiscal Year 2016: ¥6,630,000 (Direct Cost: ¥5,100,000、Indirect Cost: ¥1,530,000)
Fiscal Year 2015: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
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| Keywords | 情報検索 / コピュラ / 情報推薦 / スコア統合 / Copula / 情報システム / Top-kアルゴリズム / スコアリング |
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| Outline of Final Research Achievements |
In this research, we applied copulas which can consider complex dependencies among multiple features to the areas of information retrieval (IR) and recommender systems, and showed a method to design transparent and highly effective IR and recommender systems.
More specifically, we considered a mixture copula model which integrates multiple copulas with a linear combination for building effective IR and recommender systems. To estimate a good mixture copula which affects their effectiveness, we indicated that it is appropriate that a density-based clustering algorithm is applied in the copula estimation phase. In addition, we also developed an efficient top-k algorithm for quickly returning relevant results even if the scoring function is non-linear, such as copulas, and non-monotonic. Moreover, as for recommender systems, we showed that effective recommender systems can also be designed with mixture copulas, when preprocessing feature parameters with a statistical approach.
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