User relationship strength modeling for friend recommendation on Instagram

Dongyan Guo, Jingsong Xu, Jian Zhang, Min Xu, Ying Cui, Xiangjian He

Research output: Journal PublicationArticlepeer-review

24 Citations (Scopus)

Abstract

Social strength modeling in the social media community has attracted increasing research interest. Different from Flickr, which has been explored by many researchers, Instagram is more popular for mobile users and is conducive to likes and comments but seldom investigated. On Instagram, a user can post photos/videos, follow other users, comment and like other users’ posts. These actions generate diverse forms of data that result in multiple user relationship views. In this paper, we propose a new framework to discover the underlying social relationship strength. User relationship learning under multiple views and the relationship strength modeling are coupled into one process framework. In addition, given the learned relationship strength, a coarse-to-fine method is proposed for friend recommendation. Experiments on friend recommendations for Instagram are presented to show the effectiveness and efficiency of the proposed framework. As exhibited by our experimental results, it can obtain better performance over other related methods. Although our method has been proposed for Instagram, it can be easily extended to any other social media communities.

Original languageEnglish
Pages (from-to)9-18
Number of pages10
JournalNeurocomputing
Volume239
DOIs
Publication statusPublished - 24 May 2017
Externally publishedYes

Keywords

  • Friends recommendation
  • Multi-view learning
  • Social networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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