Model-Based Frameworks for User Adapted Information Exploration: An Overview

  • Michael Kotzyba
  • Tatiana Gossen
  • Sebastian Stober
  • Andreas Nürnberger
Chapter
Part of the Cognitive Technologies book series (COGTECH)

Abstract

The target group of search engine users in the Internet is very wide and heterogeneous. The users differ in background, knowledge, experience, etc. That is why, in order to find relevant information, such search systems not only have to retrieve web documents related to the search query but also have to consider and adapt to the user’s interests, skills, preferences and context. In addition, numerous user studies have revealed that the search process itself can be very complex, in particular if the user is not providing well-defined queries to find a specific piece of information, but is exploring the information space. This is very often the case if the user is not completely familiar with the search topic and is trying to get an overview of or learn about the topic at hand. Especially in this scenario, user- and task-specific adaptations might lead to a significant increase in retrieval performance and user experience. In order to analyze and characterize the complexity of the search process, different models for information(-seeking) behavior and information activities have been developed. In this chapter, we discuss selected models, with a focus on models that have been designed to cover the needs of individual users. Furthermore, an aggregated framework is proposed to address different levels of information(-seeking) behavior and to motivate approaches for adaptive search systems. To enable Companion-Systems to support users during information exploration, the proposed models provide solid and suitable frameworks to allow cooperative and competent assistance.

Notes

Acknowledgements

This work was done within the Transregional Collaborative Research Centre SFB/TRR 62 “Companion-Technology for Cognitive Technical Systems” funded by the German Research Foundation (DFG).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael Kotzyba
    • 1
  • Tatiana Gossen
    • 1
  • Sebastian Stober
    • 2
  • Andreas Nürnberger
    • 1
  1. 1.Data and Knowledge Engineering Group, Faculty of Computer ScienceOtto von Guericke University MagdeburgMagdeburgGermany
  2. 2.Machine Learning in Cognitive Science Lab, Research Focus Cognitive SciencesUniversity of PotsdamPotsdamGermany

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