Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning

Zhaobin Liu; Weiwei Deng; Han Chen; Jian Ma

Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning

Zhaobin Liu, Weiwei Deng^*, Han Chen, Jian Ma

^*Corresponding author for this work

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Although many patent recommendation methods have been proposed to suggest suitable patents, they aim to meet the technological needs of individual companies. Identifying the common technological needs of companies in an industrial cluster is critical. However, companies usually have privacy concerns and hesitate to reveal their technological information. Therefore, we propose a patent recommendation method based on federated learning, which learns a shared recommendation model across companies without direct access to their data and aggregates the preferences of company members in a cluster to identify common technological needs.

Original language	English
Title of host publication	ICEB 2023 PROCEEDINGS (CHIAYI, TAIWAN)
Pages	436-444
Number of pages	9
Publication status	Published - Oct 2023
Event	23rd International Conference on Electronic Business (ICEB 2023): AI and Precision Analytics in Financial and Medical Services - Hybrid, Chiayi, Taiwan Duration: 19 Oct 2023 → 23 Oct 2023 https://iceb2023.johogo.com/

Publication series

Name	Proceedings of the International Conference on Electronic Business (ICEB)
Volume	23
ISSN (Print)	1683-0040

Conference

Conference	23rd International Conference on Electronic Business (ICEB 2023)
Abbreviated title	ICEB’23
Country/Territory	Taiwan
City	Chiayi
Period	19/10/23 → 23/10/23
Internet address	https://iceb2023.johogo.com/

Research Keywords

patent recommendation
industrial cluster
federated learning
privacy preservation

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https://aisel.aisnet.org/iceb2023/38/

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Liu, Z., Deng, W., Chen, H., & Ma, J. (2023). Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning. In ICEB 2023 PROCEEDINGS (CHIAYI, TAIWAN) (pp. 436-444). Article 38 (Proceedings of the International Conference on Electronic Business (ICEB); Vol. 23). https://aisel.aisnet.org/iceb2023/38/

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title = "Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning",

abstract = "Although many patent recommendation methods have been proposed to suggest suitable patents, they aim to meet the technological needs of individual companies. Identifying the common technological needs of companies in an industrial cluster is critical. However, companies usually have privacy concerns and hesitate to reveal their technological information. Therefore, we propose a patent recommendation method based on federated learning, which learns a shared recommendation model across companies without direct access to their data and aggregates the preferences of company members in a cluster to identify common technological needs.",

keywords = "patent recommendation, industrial cluster, federated learning, privacy preservation",

author = "Zhaobin Liu and Weiwei Deng and Han Chen and Jian Ma",

year = "2023",

month = oct,

language = "English",

series = "Proceedings of the International Conference on Electronic Business (ICEB)",

pages = "436--444",

booktitle = "ICEB 2023 PROCEEDINGS (CHIAYI, TAIWAN)",

note = "23rd International Conference on Electronic Business (ICEB 2023) : AI and Precision Analytics in Financial and Medical Services, ICEB{\textquoteright}23 ; Conference date: 19-10-2023 Through 23-10-2023",

url = "https://iceb2023.johogo.com/",

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Liu, Z , Deng, W, Chen, H & Ma, J 2023, Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning. in ICEB 2023 PROCEEDINGS (CHIAYI, TAIWAN)., 38, Proceedings of the International Conference on Electronic Business (ICEB), vol. 23, pp. 436-444, 23rd International Conference on Electronic Business (ICEB 2023), Chiayi, Taiwan, 19/10/23. <https://aisel.aisnet.org/iceb2023/38/>

Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning. / Liu, Zhaobin ; Deng, Weiwei; Chen, Han et al.
ICEB 2023 PROCEEDINGS (CHIAYI, TAIWAN). 2023. p. 436-444 38 (Proceedings of the International Conference on Electronic Business (ICEB); Vol. 23).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T2 - 23rd International Conference on Electronic Business (ICEB 2023)

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AU - Deng, Weiwei

AU - Chen, Han

AU - Ma, Jian

PY - 2023/10

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N2 - Although many patent recommendation methods have been proposed to suggest suitable patents, they aim to meet the technological needs of individual companies. Identifying the common technological needs of companies in an industrial cluster is critical. However, companies usually have privacy concerns and hesitate to reveal their technological information. Therefore, we propose a patent recommendation method based on federated learning, which learns a shared recommendation model across companies without direct access to their data and aggregates the preferences of company members in a cluster to identify common technological needs.

AB - Although many patent recommendation methods have been proposed to suggest suitable patents, they aim to meet the technological needs of individual companies. Identifying the common technological needs of companies in an industrial cluster is critical. However, companies usually have privacy concerns and hesitate to reveal their technological information. Therefore, we propose a patent recommendation method based on federated learning, which learns a shared recommendation model across companies without direct access to their data and aggregates the preferences of company members in a cluster to identify common technological needs.

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KW - industrial cluster

KW - federated learning

KW - privacy preservation

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T3 - Proceedings of the International Conference on Electronic Business (ICEB)

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BT - ICEB 2023 PROCEEDINGS (CHIAYI, TAIWAN)

Y2 - 19 October 2023 through 23 October 2023

ER -

Meet the technological needs of industrial clusters with privacy concerns: A patent recommendation method based on federated learning

Abstract

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Research Keywords

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