Beyond session segmentation: predicting changes in search intent with client-side user interactions

ABSTRACT

Effective search session segmentation "grouping queries according to common task or intent" can be useful for improving relevance, search evaluation, and query suggestion. Previous work has largely attempted to segment search sessions off-line, after the fact. In contrast, we present preliminary investigation of predicting, in real time, whether a user is about to switch interest - that is, whether the user is about to finish the current search and switch to another search task (or stop searching altogether). We explore an approach for this task using client-side user behavior such as clicks, scrolls, and mouse movements, contextualized by the content of the search result pages and previous searches. Our experiments over thousands of real searches show that we can identify context and user behavior patterns that indicate that a user is about to switch to a new search task. These preliminary results can be helpful for more effective query suggestion and personalization.

REFERENCES

• 1: Richard Atterer , Monika Wnuk , Albrecht Schmidt, Knowing the user's every move: user activity tracking for website usability evaluation and implicit interaction, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135811]
• 2: Q. Guo and E. Agichtein. Exploring client-side instrumentation for personalized search intent inference. In Proc. of ITWP, 2008.
• 3: Rosie Jones , Kristina Lisa Klinkner, Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA [doi>10.1145/1458082.1458176]
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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: Relevance feedback

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

General Terms:
Design, Experimentation

Keywords:
search behavior modeling, search sessions, user intent inference