研究課題/領域番号 |
19H04215
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研究種目 |
基盤研究(B)
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配分区分 | 補助金 |
応募区分 | 一般 |
審査区分 |
小区分62020:ウェブ情報学およびサービス情報学関連
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研究機関 | 筑波大学 |
研究代表者 |
于 海涛 筑波大学, 図書館情報メディア系, 准教授 (30751052)
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研究分担者 |
吉川 正俊 大阪成蹊大学, データサイエンス学部, 教授 (30182736)
康 シン 徳島大学, 大学院社会産業理工学研究部(理工学域), 助教 (80777350)
Adam Jatowt 京都大学, 情報学研究科, 特定准教授 (00415861)
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研究期間 (年度) |
2019-04-01 – 2024-03-31
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研究課題ステータス |
交付 (2023年度)
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配分額 *注記 |
15,860千円 (直接経費: 12,200千円、間接経費: 3,660千円)
2023年度: 2,990千円 (直接経費: 2,300千円、間接経費: 690千円)
2022年度: 2,730千円 (直接経費: 2,100千円、間接経費: 630千円)
2021年度: 2,340千円 (直接経費: 1,800千円、間接経費: 540千円)
2020年度: 3,640千円 (直接経費: 2,800千円、間接経費: 840千円)
2019年度: 4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
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キーワード | privacy-preserving / federated learning / semantic matching / online learning-to-rank / interactive search / on-device learning / Metric Optimization / Policy Gradient / Differential Privacy / Privacy-preserving IR / Federated Optimization / On-device Learning / policy gradient / learning to rank / probabilistic regression / Semantic Matching / Learning-To-Rank / Metric Optimisation / Privacy-preserving / Semantic matching / Federated learning / On-device intelligence |
研究開始時の研究の概要 |
This project aims to initiate research into privacy-preserving information retrieval (IR) and develop a scalable privacy-preserving IR system. The proposed IR system builds upon a federated paradigm which fuses on-device machine learning, federated optimization and semantic matching.
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研究実績の概要 |
This year our main task is to develop the federated information retrieval model based on a large-scale dataset. To this end, we firstly view information retrieval as an interactive process between users and the search engine system rather than an independent ranking per query. Secondly, we explore how to effectively integrate online learning-to-rank and federated learning. Specifically, online learning-to-rank enables us to cope with the aforementioned interactive process. Federated learning enables us to deal with the privacy issue by learning the ranking model in an on-device manner. Based on a series of experiments on several benchmark ranking datasets, our experimental results show that this direction is applicable.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
Due to the impact of COVID-19, the number of students is limited in order to guarantee a healthy working environment when using the research room. Sometimes the research work has to be conducted either online or at home. As a result, the efficiency is impacted to some extent.
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今後の研究の推進方策 |
The major research objective of the next fiscal year is to develop the federated information retrieval model. Since the large-scale language models have achieved a revolutionary effect on many fields, including but not limited to, natural language processing and information retrieval. For the future work, we will explore whether it is possible to make full use of the newly proposed large-scale language models to enhance our research. Finally, the COVID-19 pandemic has passed away. We plan to have more on-site discussions and attend more top international conferences in order to better conduct the planned research.
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