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iPLUG: Personalized List Recommendation in Twitter

  • Lijiang Chen
  • Yibing Zhao
  • Shimin Chen
  • Hui Fang
  • Chengkai Li
  • Min Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8181)

Abstract

A Twitter user can easily be overwhelmed by flooding tweets from her followees, making it challenging for the user to find interesting and useful information in tweets. The feature of Twitter Lists allows users to organize their followees into multiple subsets for selectively digesting tweets. However, this feature has not received wide reception because users are reluctant to invest initial efforts in manually creating lists. To address the challenge of bootstrapping Twitter Lists, we envision a novel tool that automatically creates personalized Twitter Lists and recommends them to users. Compared with lists created by real Twitter users, the lists generated by our algorithms achieve 73.6% similarity.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lijiang Chen
    • 1
  • Yibing Zhao
    • 2
  • Shimin Chen
    • 3
  • Hui Fang
    • 4
  • Chengkai Li
    • 5
  • Min Wang
    • 6
  1. 1.HP LabsBeijingChina
  2. 2.John Hopkins UniversityBaltimoreUSA
  3. 3.Chinese Academy of SciencesBeijingChina
  4. 4.University of DelawareNewarkUSA
  5. 5.University of Texas at ArlingtonArlingtonUSA
  6. 6.Google ResearchMountain ViewUSA

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