Corpus ID: 37828241

Describing user's search behaviour with Hidden Markov Models

@article{Dungs2016DescribingUS,
  title={Describing user's search behaviour with Hidden Markov Models},
  author={S. Dungs},
  journal={Bull. IEEE Tech. Comm. Digit. Libr.},
  year={2016},
  volume={12}
}
  • S. Dungs
  • Published 2016
  • Computer Science
  • Bull. IEEE Tech. Comm. Digit. Libr.
  • This paper introduces Hidden Markov Models (HMM) for modelling user’s search behaviour in a digital library search scenario. Three potential applications of these models have been identified in the literature: System evaluation, simulation of user activity and user guidance. Models have been generated from 36 eye tracking log files, where smaller models up to five hidden states provided best prediction quality to complexity ratio. While in its current state this work makes simplifications… CONTINUE READING
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