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MATAR: Keywords Enhanced Multi-label Learning for Tag Recommendation

  • Licheng LiEmail author
  • Yuan Yao
  • Feng Xu
  • Jian Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9313)

Abstract

Tagging is a popular way to categorize and search online content, and tag recommendation has been widely studied to better support automatic tagging. In this work, we focus on recommending tags for content-based applications such as blogs and question-answering sites. Our key observation is that many tags actually have appeared in the content in these applications. Based on this observation, we first model the tag recommendation problem as a multi-label learning problem and then further incorporate keyword extraction to improve recommendation accuracy. Moreover, we speedup the proposed method using a locality-sensitive hashing strategy. Experimental evaluations on two real data sets demonstrate the effectiveness and efficiency of our proposed methods.

Keywords

Tag recommendation multi-label learning keyword extraction locality-sensitive hashing 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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