搜索互动的成本与效益分析

L. Azzopardi, G. Zuccon
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引用次数: 28

摘要

交互式信息检索(IR)系统通常提供各种特性和功能,如查询建议和相关性反馈,用户可以决定是否使用这些特性和功能。做出这种选择的决定会产生相关成本,也可能带来一些好处。因此,精明的用户会做出净收益最大化的决定。在本文中,我们对用户在搜索时隐含或明确做出的各种决定的成本和收益进行了正式建模。我们考虑并分析了以下情况:(i) 用户的查询应该多长?(ii) 用户应提出具体还是模糊的查询?(iii) 用户应该接受建议还是重新表述?(iv) 用户何时应使用相关性反馈? (v) 何时 "查找相似 "功能对用户有价值?为此,我们建立了一系列成本效益模型,探索影响决策的各种参数。通过分析,我们能够深入了解不同的决策,为观察到的行为提供解释,并提出许多可检验的假设。这项工作不仅为今后的实证工作奠定了基础,还为开发其他涉及人机交互的成本效益模型提供了模板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Analysis of the Cost and Benefit of Search Interactions
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.
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