TY - GEN
T1 - An analysis of the cost and benefit of search interactions
AU - Azzopardi, Leif
AU - Zuccon, Guido
N1 - © 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval http://dx.doi.org/10.1145/2970398.2970412.
PY - 2016/9/12
Y1 - 2016/9/12
N2 - 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.
AB - 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.
KW - evaluation
KW - measures
KW - retrieval strategies
KW - search behaviour
KW - user models
KW - interactive information retrieval systems
KW - query suggestions
KW - relevance feedback
KW - cost-benefit models
KW - human-computer interaction
UR - http://ictir2016.org/
UR - http://dl.acm.org/citation.cfm?id=2970398
U2 - 10.1145/2970398.2970412
DO - 10.1145/2970398.2970412
M3 - Conference contribution book
SN - 9781450344975
SP - 59
EP - 68
BT - ICTIR '16 Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval
CY - New York,
ER -