Analyzing and Learning from User Interactions for Search Clarification
Analyzing and Learning from User Interactions for Search Clarification
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DOI：
10.1145/3397271.3401160
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发表时间：
2020-05
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通讯作者：
Hamed Zamani;Bhaskar Mitra;Everest Chen;Gord Lueck;Fernando Diaz;Paul N. Bennett;Nick Craswell;S. Dumais
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文献类型：
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作者：
Hamed Zamani;Bhaskar Mitra;Everest Chen;Gord Lueck;Fernando Diaz;Paul N. Bennett;Nick Craswell;S. Dumais
回答搜索查询时提出澄清问题已被认为是揭示查询潜在意图的一种有用技术。Clarity在具有不同接口的检索系统中有应用，从传统的网络搜索接口到有限带宽接口，如纯语音和小屏幕设备。澄清问题的产生和评估最近在文献中得到了研究。然而，用户与澄清问题的交互相对来说还没有被探索过。在本文中，我们通过分析大型网络搜索引擎中的大规模用户交互来澄清问题，进行了全面的研究。更详细地，我们通过根据搜索查询的不同属性澄清问题、澄清问题及其候选答案来分析收到的用户参与。我们进一步研究了数据中的点击偏差，结果表明，即使阅读澄清问题和候选答案并不需要付出太大的努力，数据中仍然存在一些位置和呈现偏差。我们还提出了一种基于用户交互数据作为隐式反馈的问题澄清的学习表示模型。该模型用于对给定查询的多个自动生成的澄清问题进行重新排序。在点击数据和人工标注数据上的测试结果表明，该方法具有较高的质量。
Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications in retrieval systems with different interfaces, from the traditional web search interfaces to the limited bandwidth interfaces as in speech-only and small screen devices. Generation and evaluation of clarifying questions have been recently studied in the literature. However, user interaction with clarifying questions is relatively unexplored. In this paper, we conduct a comprehensive study by analyzing large-scale user interactions with clarifying questions in a major web search engine. In more detail, we analyze the user engagements received by clarifying questions based on different properties of search queries, clarifying questions, and their candidate answers. We further study click bias in the data, and show that even though reading clarifying questions and candidate answers does not take significant efforts, there still exist some position and presentation biases in the data. We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback. The model is used for re-ranking a number of automatically generated clarifying questions for a given query. Evaluation on both click data and human labeled data demonstrates the high quality of the proposed method.