| Item type |
FIT(1) |
| 公開日 |
2010-08-20 |
| タイトル |
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言語 |
en |
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タイトル |
F-036 Online Rank Aggregation |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
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資源タイプ |
conference paper |
| 著者所属 |
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九大 |
| 著者所属 |
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九大 |
| 著者所属 |
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九大 |
| 著者所属 |
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九大 |
| 著者所属(英) |
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en |
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Department of Informatics, Kyushu University |
| 著者所属(英) |
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en |
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Department of Informatics, Kyushu University |
| 著者所属(英) |
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en |
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Department of Informatics, Kyushu University |
| 著者所属(英) |
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en |
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Department of Informatics, Kyushu University |
| 著者名 |
安武, 翔太
畑埜, 晃平
瀧本, 英二
竹田, 正幸
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| 著者名(英) |
Yasutake, Shota
Hatano, Kohei
Takimoto, Eiji
Takeda, Masayuki
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| 論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
We consider an online learning framework where the task is to predict a permutation which represents a ranking of n fixed objects. At each trial, the learner incurs a loss defined as the Kendall tau distance between the predicted permutation and the true permutation given by the adversary. This setting is quite natural in many situations such as information retrieval and recommendation tasks. We propose algorithms for this problem and prove relative loss bounds with regret only depending on O(n^2). Further, we also prove a matching lower bound of the regret, which shows our algorithms are almost optimal. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA1242354X |
| 書誌情報 |
情報科学技術フォーラム講演論文集
巻 9,
号 2,
p. 441-448,
発行日 2010-08-20
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| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
KJ00006917153.pdf (631.9 kB)
