Combining evidence for automatic web session identification

  • Authors:

  • Daqing He;Ayse Göker;David J. Harper

  • Affiliations:

  • School of Computer and Mathematical Science, The Robert Gordon University, St. Andrew Street, Aberdeen AB25 1HG, Scotland, UK;School of Computer and Mathematical Science, The Robert Gordon University, St. Andrew Street, Aberdeen AB25 1HG, Scotland, UK;School of Computer and Mathematical Science, The Robert Gordon University, St. Andrew Street, Aberdeen AB25 1HG, Scotland, UK

  • Venue:
  • Information Processing and Management: an International Journal - Issues of context in information retrieval
  • Year:
  • 2002

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Abstract

Contextual information provides an important basis for identifying and understanding users' information needs. Our previous work in traditional information retrieval systems has shown how using contextual information could improve retrieval performance. With the vast quantity and variety of information available on the Web, and the short query lengths within Web searches, it becomes even more crucial that appropriate contextual information is extracted to facilitate personalized services. However, finding users' contextual information is not straightforward, especially in the Web search environment where less is known about the individual users. In this paper, we will present an approach that has significant potential far studying Web users' search contexts. The approach automatically groups a user's consecutive search activities on the same search topic into one session. It uses Dempster-Shafer theory to combine evidence extracted from two sources, each of which is based on the statistical data from Web search logs. The evaluation we have performed demonstrates that our approach has achieved a significant improvement over previous methods of session identification.