A cross-platform recommendation system from Facebook to Instagram

Chia Ling Chang, Yen Liang Chen, Jia Shin Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Purpose: 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.

Original languageEnglish
Pages (from-to)264-285
Number of pages22
JournalElectronic Library
Issue number2-3
StatePublished - 24 May 2023


  • Cross-platform recommendation system
  • Facebook
  • Instagram
  • Social media


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