Automatically identifying localizable queries

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

General Terms:
Experimentation, Human Factors, Measurement

Keywords:
localizable query, machine learning, web search