An analysis of the cost and benefit of search interactions

Leif Azzopardi, Guido Zuccon

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

16 Citations (Scopus)
66 Downloads (Pure)

Abstract

Interactive Information Retrieval (IR) systems often provide various features and functions, such as query suggestions and relevance feedback, that a user may or may not decide to use. The decision to take such an option has associated costs and may lead to some benefit. Thus, a savvy user would take decisions that maximises their net benefit. In this paper, we formally model the costs and benefits of various decisions that users, implicitly or explicitly, make when searching. We consider and analyse the following scenarios: (i) how long a user's query should be? (ii) should the user pose a specific or vague query? (iii) should the user take a suggestion or re-formulate? (iv) when should a user employ relevance feedback? and (v) when would the "find similar" functionality be worthwhile to the user? To this end, we build a series of cost-benefit models exploring a variety of parameters that affect the decisions at play. Through the analyses, we are able to draw a number of insights into different decisions, provide explanations for observed behaviours and generate numerous testable hypotheses. This work not only serves as a basis for future empirical work, but also as a template for developing other cost-benefit models involving human-computer interaction.
Original languageEnglish
Title of host publicationICTIR '16 Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval
Place of PublicationNew York,
Pages59-68
Number of pages10
DOIs
Publication statusPublished - 12 Sep 2016

Keywords

  • evaluation
  • measures
  • retrieval strategies
  • search behaviour
  • user models
  • interactive information retrieval systems
  • query suggestions
  • relevance feedback
  • cost-benefit models
  • human-computer interaction

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