Assessing the contribution of twitters textual information to graph-based recommendation

Evgenia Wasserman Pritsker, Tsvi Kuflik, Einat Minkov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Graph-based recommendation approaches can model associations between users and items alongside additional contextual information. Recent studies demonstrated that representing features extracted from social media (SM) auxiliary data, like friendships, jointly with traditional users/items ratings in the graph, contribute to recommendation accuracy. In this work, we take a step further and propose an extended graph representation that includes socio-demographic and personal traits extracted from the content posted by the user on SM. Empirical results demonstrate that processing unstructured textual information collected from Twitter and representing it in structured form in the graph improves recommendation performance, especially in cold start conditions.

Original languageEnglish
Title of host publicationIUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450343480
StatePublished - 7 Mar 2017
Event22nd International Conference on Intelligent User Interfaces, IUI 2017 - Limassol, Cyprus
Duration: 13 Mar 201716 Mar 2017

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


Conference22nd International Conference on Intelligent User Interfaces, IUI 2017


  • Graph-based recommendation
  • Information extraction
  • PPR
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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