Department of Information and Library Science, Tamkang University Full-Time Faculty

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TitleA cross-platform recommendation system from Facebook to Instagram

Year111

Semester2

Publish Date2023/03/31

Journal NameA cross-platform recommendation system from Facebook to Instagram

Journal Name Other

All AuthorChang, Chia-Ling;Chen, Yen-Liang;Li, Jia-Shin

Unit

Publisher

VolumeThe Electronic Library、Vol. 41 No. 2/3、264 - 285

SummaryPurpose The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users. Design/methodology/approach We collect data from both Facebook and Instagram and then propose a similarity matching mechanism for recommending the most appropriate Instagram accounts to Facebook users. By removing the data disparity between the two heterogeneous platforms and integrating them, the system is able to make more accurate recommendations. Findings The results show that the method proposed in this paper can recommend suitable public Instagram accounts to Facebook users with very high accuracy. Originality/value To the best of the authors’ knowledge, this is the first study to propose a recommender system to recommend Instagram public accounts to Facebook users. Second, our proposed method can integrate heterogeneous data from two different platforms to generate collaborative recommendations. Furthermore, our cross-platform system reveals an innovative concept of how multiple platforms can promote their respective platforms in a unified, cooperative and collaborative manner.

KeywordCross-platform recommendation system;Social media;Facebook;Instagram

Use LangEnglish

ISSN0264-0473

Journalnature國外

Included in,SSCI

UniversityCooperation

CorrespondingAuthor

Reviewsystem否

Country美國

Open Call for Papers

PublicationStyle電子版

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