User Modeling on Twitter with Exploiting Explicit Relationships for Personalized Recommendations

Abduallah Alshammari, Stelios Kapetanakis, Roger Evans, Nikolaos Polatidis, Gharbi Alshammari

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


The use of social networks sites has led to a challenging overload of information that helped new social networking sites such as Twitter to become popular. It is believed that Twitter provides a rich environment for shared information that can help with recommender systems research. In this paper, we study Twitter user modeling by utilizing explicit relationships among users. This work aims to build personal profiles through a alternative methods using information gained from Twitter to provide more accurate recommendations. Our method exploits the explicit relationships of a Twitter user to extract information that is important in building the user’s personal profile. The usefulness of this proposed method is validated by implementing a tweet recommendation service and by performing offline evaluation. We compare our proposed user profiles against other profiles such as a baseline using cosine similarity measures to check the effectiveness of the proposed method. The performance is measured on an adequate number of users.

Original languageEnglish
Title of host publicationHybrid Intelligent Systems - 18th International Conference on Hybrid Intelligent Systems HIS 2018
EditorsMaria Leonilde Varela, Ajith Abraham, Niketa Gandhi, Ana Maria Madureira
Place of PublicationCham
Number of pages11
ISBN (Electronic)9783030143473
ISBN (Print)9783030143466
Publication statusPublished - 21 Mar 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


  • Explicit relationships
  • Influence score
  • Recommender systems
  • Twitter
  • User modeling
  • User profiling


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