| Project/Area Number |
19H04215
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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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| Review Section |
Basic Section 62020:Web informatics and service informatics-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Yu Haitao 筑波大学, 図書館情報メディア系, 准教授 (30751052)
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| Co-Investigator(Kenkyū-buntansha) |
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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| Project Period (FY) |
2019-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥15,860,000 (Direct Cost: ¥12,200,000、Indirect Cost: ¥3,660,000)
Fiscal Year 2023: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2022: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2021: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2020: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2019: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
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| Keywords | Federated Learning / Large Language Model / Conversational IR / Generative IR / Personalization / On-device Learning / User Modeling / Neural Tree Ensemble / Neural Tree Ensembles / 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 / policy gradient / learning to rank / probabilistic regression / Semantic Matching / Learning-To-Rank / Metric Optimisation / Privacy-preserving / Semantic matching / Federated learning / On-device intelligence |
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| Outline of Research at the Start |
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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| Outline of Final Research Achievements |
This project aims to initiate research into privacy-preserving information retrieval (IR). Throughout the lifetime of this project, we made remarkable achievements via the following main aspects: novel ways of combining federated learning and differential privacy for privacy-preserving information access, direct optimization of evaluation metrics, effective integration of LLMs for result re-ranking, incorporating personalized context for conversational information seeking. As a result, we published more than 20 conference papers and 10 journal papers. Moreover, we are also maintaining an open-source project for IR named as PTRanking, which includes many representative ranking methods based on neural networks. Overall, it is reasonable to say that the successful accomplishment of this project will bring new insights into the development of privacy-preserving IR techniques.
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| Academic Significance and Societal Importance of the Research Achievements |
Our research achievements would deepen the understanding of privacy-preserving information seeking that goes beyond information retrieval (IR). By releasing the source codes and collections, we encourage the entire IR community to improve the research of privacy-preserving IR towards new stages.
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