Using web browser interactions to predict task

ABSTRACT

The automatic identification of a user's task has the potential to improve information filtering systems that rely on implicit measures of interest and whose effectiveness may be dependant upon the task at hand. Knowledge of a user's current task type would allow information filtering systems to apply the most useful measures of user interest. We recently conducted a field study in which we logged all participants' interactions with their web browsers and asked participants to categorize their web usage according to a high-level task schema. Using the data collected during this study, we have conducted a preliminary exploration of the usefulness of logged web browser interactions to predict users' tasks. The results of this initial analysis suggest that individual models of users' web browser interactions may be useful in predicting task type.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.1 Models
          Subjects: Structural

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.4 Hypertext/Hypermedia
          Subjects: Navigation

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
decision tree, field study, information filtering, task, task prediction, web