Topic-model based query intent prediction for search by multiple examples

Mingzhu Zhu, Chao Xu, Yi Fang Brook Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

It is often difficult for users to express their information needs as keywords. Search-By-Multiple-Examples (SBME) is a new search paradigm that provides an easier way for users to express their information needs as multiple relevant examples (called query examples) rather than as a simple string of keywords. One key issue of SBME is to identify user’s true information needs from the query examples, which may contain multiple topics. However, none of the previous studies on SBME considers the issue of topic diversity of the query examples. In this research, we explore the solutions to the topic diversity issue in SBME through adopting topic modeling techniques to predict the likelihood that the query examples belonging to a topic. The learned topic distributions from the query examples are used to build query vectors for document ranking. Using Mean Average Precision (MAP) and precision at k (p@k), experiments conducted on two benchmark datasets show that the proposed method outperforms the two baselines significantly, especially when a large number of query examples are available.

Original languageEnglish (US)
Title of host publicationProceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014
EditorsHamid R. Arabnia, David de la Fuente, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M.G. Solo, Fernando G. Tinetti
PublisherCSREA Press
Pages141-146
Number of pages6
ISBN (Electronic)1601322763, 9781601322760
StatePublished - Jan 1 2014
Event2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014 - Las Vegas, United States
Duration: Jul 21 2014Jul 24 2014

Publication series

NameProceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014

Conference

Conference2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014
CountryUnited States
CityLas Vegas
Period7/21/147/24/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • Information need
  • Information retrieval
  • Search by multiple examples
  • Topic modeling

Fingerprint Dive into the research topics of 'Topic-model based query intent prediction for search by multiple examples'. Together they form a unique fingerprint.

Cite this