A language model approach for tag recommendation

  • Authors:

  • Ke Sun;Xiaolong Wang;Chengjie Sun;Lei Lin

  • Affiliations:

  • School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China and Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, Ch ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

Quantified Score

Hi-index 12.05

Visualization

Abstract

Tags are user-generated keywords for entities. Recently tags have been used as a popular way to allow users to contribute metadata to large corpora on the web. However, tagging style websites lack the function of guaranteeing the quality of tags for other usages, like collaboration/community, clustering, and search, etc. Thus, as a remedy function, automatic tag recommendation which recommends a set of candidate tags for user to choice while tagging a certain document has recently drawn many attentions. In this paper, we introduce the statistical language model theory into tag recommendation problem named as language model for tag recommendation (LMTR), by converting the tag recommendation problem into a ranking problem and then modeling the correlation between tag and document with the language model framework. Furthermore, we leverage two different methods based on both keywords extraction and keywords expansion to collect candidate tag before ranking with LMTR to improve the performance of LMTR. Experiments on large-scale tagging datasets of both scientific and web documents indicate that our proposals are capable of making tag recommendation efficiently and effectively.