Automatically identifying localizable queries

  • Authors:

  • Michael J. Welch;Junghoo Cho

  • Affiliations:

  • University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA

  • Venue:
  • Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

Personalization of web search results as a technique for improving user satisfaction has received notable attention in the research community over the past decade. Much of this work focuses on modeling and establishing a profile for each user to aid in personalization. Our work takes a more query-centric approach. In this paper, we present a method for efficient, automatic identification of a class of queries we define as localizable from a web search engine query log. We determine a set of relevant features and use conventional machine learning techniques to classify queries. Our experiments find that our technique is able to identify localizable queries with 94% accuracy.