搜索的经济模型

L. Azzopardi
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引用次数: 7

摘要

搜索本质上是一个交互过程,通常需要提交大量查询和评估大量文档,以便找到所需数量的相关信息。虽然已经提出了许多搜索模型,但它们在本质上主要是概念性的,提供了搜索过程的描述性说明。例如,贝茨的浆果采摘比喻恰当地描述了信息搜寻者如何搜寻相关信息。然而,它缺乏任何预测或解释能力。在这次演讲中,我将概述微观经济理论如何应用于交互式信息检索,其中搜索过程可以被视为用于“产生”输出(即相关性)的输入(即查询和评估)的组合。在这种观点下,建立模型不仅可以描述交互、成本和收益之间的关系,还可以解释和预测行为。在演讲中,我将通过一些例子来说明经济学如何解释不同的行为。例如,为什么博士生应该比他们的导师搜索更多(使用Cooper开发的经济模型b[6]),为什么查询很短b[1],为什么布尔搜索者需要探索更多的结果,以及为什么在搜索网络时查看前几个结果是可以的b[2]。然后,我将描述不同交互的成本如何影响搜索行为[3],然后扩展当前的理论,包括其他变量(如在搜索结果页面上花费的时间,与片段的交互等),以创建更复杂和现实的模型。从本质上讲,我认为通过使用这些模型,我们可以:理论化并预测用户在与系统交互时的行为;确定不同交互的成本将如何影响搜索行为;理解为什么特定的交互风格、策略、技术会被用户采用或不被用户采用;根据预期收益和相关成本,确定哪些交互和功能值得使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Economic models of search
Searching is inherently an interactive process usually requiring a number of queries to be submitted and a number of documents to be assessed in order to find the desired amount of relevant information. While numerous models of search have been proposed, they have been largely conceptual in nature providing a descriptive account of the search process. For example, Bates' Berry Picking metaphor aptly describes how information seekers forage for relevant information [4]. However it lacks any predictive or explanatory power. In this talk, I will outline how microeconomic theory can be applied to interactive information retrieval, where the search process can be viewed as a combination of inputs (i.e. queries and assessments) which are used to "produce" output (i.e. relevance). Under this view, it is possible to build models that not only describe the relationship between interaction, cost and gain, but also explain and predict behaviour. During the talk, I will run through a number of examples of how economics can explain different behaviours. For example, why PhD students should search more than their supervisors (using an economic model developed by Cooper [6]), why queries are short [1], why Boolean searchers need to explore more results, and why it is okay to look at the first few results when searching the web [2]. I shall then describe how the cost of different interactions affect search behaviour [3], before extending the current theory to include other variables (such as the time spent on the search result page, the interaction with snippets, etc) to create more sophisticated and realistic models. Essentially, I will argue that by using such models we can: 1. theorise and predict how users will behave when interacting with systems, 2. ascertain how the costs of different interaction will influence search behaviour, 3. understand why particular interaction styles, strategies, techniques are or are not adopted by users, and, 4. determine what interactions and functionalities are worthwhile based on their expected gain and associated costs.
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