Finding Related Micro-blogs Based on WordNet

  • Lin Li
  • Huifan Xiao
  • Guandong Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7240)

Abstract

In the common formulation, the recommendation problem is reduced to the problem of estimating the utilization for the items that have not been seen by a user [1]. Micro-blog recommendation will recommend micro-blogs interest users, mostly those related to the micro-blogs that a user had issued or trending topics. One indispensable step in realizing effective recommendation is to compute short text similarities between micro-blogs. In this paper, we utilize two kinds of approaches, traditional cosine-based approach and WordNet-based semantic approach, to compute similarities between micro-blogs and recommend top related ones to users. We conduct experimental study on the effectiveness of two approaches using a set of evaluation measures. The results show that semantic similarity based approach has relatively higher precision than that of traditional cosine-based method using 548 twitters as dataset.

Keywords

Semantic Similarity Semantic Relation Query Expansion Short Text Find Relate 
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 2012

Authors and Affiliations

  • Lin Li
    • 1
  • Huifan Xiao
    • 1
  • Guandong Xu
    • 2
  1. 1.School of Computer Science & TechnologyWuhan University of TechnologyWuhanChina
  2. 2.Centre for Applied InformaticsVictoria UniversityVictoriaAustralia

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