Building Economic Models and Measures of Search

被引:3
作者
Azzopardi, Leif [1 ]
Moffat, Alistair [2 ]
Thomas, Paul [3 ]
Zuccon, Guido [4 ]
机构
[1] Univ Strathclyde, Glasgow, Lanark, Scotland
[2] Univ Melbourne, Melbourne, Vic, Australia
[3] Microsoft, Canberra, ACT, Australia
[4] Univ Queensland, Brisbane, Qld, Australia
来源
PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19) | 2019年
关键词
PROBABILITY RANKING PRINCIPLE;
D O I
10.1145/3331184.3331379
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Economics provides an intuitive and natural way to formally represent the costs and benefits of interacting with applications, interfaces and devices. By using economic models it is possible to reason about interaction, make predictions about how changes to the system will affect behavior, and measure the performance of people's interactions with the system. In this tutorial, we first provide an overview of relevant economic theories, before showing how they can be applied to formulate different ranking principles to provide the optimal ranking to users. This is followed by a session showing how economics can be used to model how people interact with search systems, and howto use these models to generate hypotheses about user behavior. The third session focuses on how economics has been used to underpin the measurement of information retrieval systems and applications using the C/W/L framework (which reports the expected utility, expected total utility, expected total cost, and so on) - and how different models of user interaction lead to different metrics. We then show how information foraging theory can be used to measure the performance of an information retrieval system - connecting the theory of how people search with how we measure it. The final session of the day will be spent building economic models and measures of search. Here sample problems will be provided to challenge participants, or participants can bring their own.
引用
收藏
页码:1401 / 1402
页数:2
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