Towards Effective Recommendation of Social Data across Social Networking Sites

  • Yuan Wang
  • Jie Zhang
  • Julita Vassileva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6304)


Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter, are often overwhelmed by the huge amount of social data (friends’ updates and other activities). We propose using machine learning techniques to learn preferences of users and generate personalized recommendations. We apply four different machine learning techniques on previously rated activities and friends to generate personalized recommendations for activities that may be interesting to each user. We also use different non-textual and textual features to represent activities. The evaluation results show that good performance can be achieved when both non-textual and textual features are used, thus helping users deal with cognitive overload.


Textual Feature Recommender System Social Networking Site Machine Learning Technique Term Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yuan Wang
    • 1
  • Jie Zhang
    • 2
  • Julita Vassileva
    • 1
  1. 1.Department of Computer ScienceUniversity of SaskatchewanCanada
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

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