Recommendations in signed social networks

Jiliang Tang, Charu Aggarwal, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations


Recommender systems play a crucial role in mitigating the information overload problem in social media by suggesting relevant information to users. The popularity of pervasively available social activities for social media users has encouraged a large body of literature on exploiting social networks for recommendation. The vast majority of these systems focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks (or social networks with positive and negative links). The availability of negative links in signed social networks presents both challenges and opportunities in the recommendation process. We provide a principled and mathematical approach to exploit signed social networks for recommendation, and propose a model, RecSSN, to leverage positive and negative links in signed social networks. Empirical results on real-world datasets demonstrate the effectiveness of the proposed framework. We also perform further experiments to explicitly understand the effect of signed networks in RecSSN.

Original languageEnglish (US)
Title of host publication25th International World Wide Web Conference, WWW 2016
PublisherInternational World Wide Web Conferences Steering Committee
Number of pages10
ISBN (Electronic)9781450341431
StatePublished - Jan 1 2016
Externally publishedYes
Event25th International World Wide Web Conference, WWW 2016 - Montreal, Canada
Duration: Apr 11 2016Apr 15 2016


Other25th International World Wide Web Conference, WWW 2016


  • Negative Links
  • Signed Networks
  • Social Recommendation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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