Attribute-driven hidden markov model trees for intention prediction

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


In this paper, we introduce a novel approach to generate an intention prediction model of user interactions with systems. As part of this new approach, we include personal aspects, such as user characteristics, that can increase prediction accuracy. The model is automatically trained according to the users fixed attributes (e.g., demographic data such as age and gender) and the users sequences of actions in the system. The generated model has a tree structure. The building blocks of each node can be any probabilistic sequence model such as hidden Markov models (HMMs) and conditional random fields (CRFs) and each node is split according to user attributes. Thus, we refer to this algorithm as an attribute-driven model tree. The new model was first tested on simulated data in which users with different attributes (such as age and gender) behave differently when trying to accomplish various tasks. We then validated the ability of the algorithm to discover the relevant attributes. We tested our algorithm on two real datasets: from a web application and a mobile application dataset. The results were encouraging and indicate the capability of the proposed method to discover the correct user intention model and increasing intention prediction accuracy compared with single HMM or CRF models.

Original languageEnglish
Article number6212389
Pages (from-to)1103-1119
Number of pages17
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number6
StatePublished - 13 Jun 2012


  • Hidden Markov model (HMM)
  • intention prediction
  • sequence learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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