Using metadata for query refinement and recommendation

Jian Qin, Xiaozhong Liu, Xia Lin, Miao Chen

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

Lengthy lists of search results are the fruit of both short queries and conventional Web search result displays. They are problematic for meeting user's information needs. This paper describes the first part, topic extraction and representation from metadata, of a project that will develop an interactive visual query refinement and recommendation (QRR) service to alleviate the problems due to lengthy lists of search results. The topic extraction uses the Latent Dirichlet Allocation (LDA) algorithm to mine the intra- and inter-document relations and represent them in topic and features. The paper presents how the LDA algorithm extracts topics and features from metadata records contained in NSDL search results, which will be used by an interactive QRR service in the next step of the project.

Original languageEnglish (US)
Pages (from-to)15-20
Number of pages6
JournalProceedings of the International Conference on Dublin Core and Metadata Applications
StatePublished - Dec 1 2009
Event9th International Conference on Dublin Core and Metadata Applications, DC-2009 - Seoul, Korea, Republic of
Duration: Oct 12 2009Oct 16 2009

Keywords

  • LDA algorithm
  • Query refinement
  • Query refinement and recommendation (QRR)
  • Topic detection and tracking

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

Fingerprint Dive into the research topics of 'Using metadata for query refinement and recommendation'. Together they form a unique fingerprint.

Cite this